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NeuralNetwork/NeuralNetwork.py

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+import os
+import warnings
+import pandas as pd
+import numpy as np
+from scipy.stats import pearsonr
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPRegressor
+from sklearn.metrics import mean_squared_error
+from sklearn.exceptions import ConvergenceWarning
+from matplotlib import pyplot as plt
+
+
+
+class My_NeuralNetwork:
+    def __init__(self):
+        self.MAX_LAYERS = 10
+        self.target_column = "Water_Intake (liters)"
+        self.model = None
+
+        # default parameters
+        self.num_layer = 3
+        self.dimension = (32, 32, 32)
+        self.correlation_treshold = 0.01
+        self.epochs = 300
+
+        # Load the dataset and preprocess it
+        csv_file = os.path.join("app", "data", "gym_members_exercise_tracking.csv")
+        df = pd.read_csv(csv_file, engine="python")
+
+        df = df.dropna()  # Remove rows with any null cell (just in case)
+        # Assigning some of the features as Category.
+        df["Gender"] = df["Gender"].astype("category")
+        df["Workout_Type"] = df["Workout_Type"].astype("category")
+
+        # getting the names of the numerical and categorical columns for later
+        numeric_columns = list(df.select_dtypes(exclude=["category"]).columns)
+        categorical_columns = list(df.select_dtypes(include=["category"]).columns)
+
+        self.df_original = (
+            df.copy()
+        )  # the df variable will have some features removes later but not this one
+
+        # remove the target to the list of features
+        numeric_columns.remove(self.target_column)
+
+        self.scaler = MinMaxScaler()  # create a new MinMaxScaler
+        df_scaled = self.scaler.fit_transform(
+            df[numeric_columns]
+        )  # scale all the numerical columns using the new MinMaxScaler
+        df[numeric_columns] = df_scaled.copy()
+
+        df_encoded = pd.get_dummies(
+            df, columns=categorical_columns, drop_first=False
+        )  # get one-hot encoding for all categorical values
+        df_encoded = df_encoded.astype(
+            float
+        )  # convert the one-hot encoding into floats (values between 0.0 - 1.0)
+        new_columns = list(
+            set(df_encoded.columns) - set(numeric_columns)
+        )  # get the list of the new columns (former categorical columns)
+        df = (
+            df_encoded.copy()
+        )  # the dataframe is now the one with all the one-hot encoded features
+        numeric_columns.extend(
+            new_columns
+        )  # add the new columns to the list of numerical columns
+
+        self.numeric_columns = numeric_columns
+        self.df = df
+
+    def train_model(self):
+        # FEATURE SELECTION
+
+        correlation_matrix = self.df[self.numeric_columns].corr()
+        # calculating the Pearson’s correlation coefficient p-value for each element in the matrix
+        p_values = pd.DataFrame(
+            np.zeros((len(self.numeric_columns), len(self.numeric_columns))),
+            columns=self.numeric_columns,
+            index=self.numeric_columns,
+        )
+        # Calculate p-values for each pair
+        for col1 in self.numeric_columns:
+            for col2 in self.numeric_columns:
+                if col1 != col2:
+                    _, p_value = pearsonr(
+                        self.df[col1], self.df[col2]
+                    )  # using scipy.stats.pearsonr to get the p-value for one pair of feature
+                    p_values.loc[col1, col2] = p_value
+                else:
+                    p_values.loc[col1, col2] = (
+                        1  # Set to 1 to not get the relation when trying to find correlations between features
+                    )
+
+        # Identifying variables that are correlated
+        # When the p-value is smaller than 0.05, there is likely a “real” relationship between the variables.
+        correlated_columns = []
+        for i, col1 in enumerate(self.numeric_columns):
+            for j, col2 in enumerate(self.numeric_columns):
+                if (
+                    j > i
+                    and p_values.loc[col1, col2] < 0.05
+                    and col1 != self.target_column
+                    and col2 != self.target_column
+                ):
+                    correlated_columns.append((col1, col2, p_values.loc[col1, col2]))
+        
+        # remove the target to the list of features
+        self.numeric_columns.remove(self.target_column)
+
+        # Identify features with a low correlation with the target
+        target_corr = correlation_matrix[self.target_column].copy()
+        correlation_treshold = self.correlation_treshold
+        features_to_remove = target_corr[abs(target_corr) < correlation_treshold].index
+        features_to_remove = set(features_to_remove.to_list())
+
+        # Identify redundant features using p-values
+        x = {"keep": set(), "remove": set()}
+        for corr_duo in correlated_columns:
+            # put the feature with the highest correlation to the target variable in "keep" and the other one in "remove"
+            if target_corr[corr_duo[0]] > target_corr[corr_duo[1]]:
+                x["keep"].add(corr_duo[0])
+                x["remove"].add(corr_duo[1])
+            else:
+                x["keep"].add(corr_duo[1])
+                x["remove"].add(corr_duo[0])
+
+        # remove features that are already removed from "keep"
+        x["keep"] = x["keep"] - features_to_remove
+
+        # remove features that are in "remove" and not in "keep"
+        redundant_features = x["remove"] - x["keep"]
+
+        features_to_remove = features_to_remove.union(redundant_features)
+
+        # Remove the selected features from the dataframe
+        for feature in list(features_to_remove):
+            self.numeric_columns.remove(feature)
+            self.df.drop(feature, axis=1, inplace=True)
+        self.features_to_remove = features_to_remove
+
+        print(
+            f"List of numerical features that will be used to predict the target ({self.target_column}) :"
+        )
+        print(self.numeric_columns)
+
+        # CREATE & TRAIN MODEL
+
+        # split the data
+        X = self.df[self.numeric_columns]
+        y = self.df[self.target_column]
+
+        # 20% of the dataset will be use as test data
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+        self.model = MLPRegressor(
+            hidden_layer_sizes=self.dimension,
+            activation="relu",  # ReLU activation function
+            solver="adam",  # Adam optimizer
+            max_iter=1,  # One iteration per fit call since the training loop is defined below
+            warm_start=True,
+        )  # Used to measure the MSE throughout the iterations
+
+        # ignoring the warning raised because we're using a manual loop for training
+        warnings.filterwarnings("ignore", category=ConvergenceWarning)
+
+        # Track MSE values during training
+        mse_values = []
+        epochs = self.epochs
+
+        for epoch in range(epochs):
+            # Train the model
+            self.model.fit(X_train, y_train)
+
+            # Predict on the test set
+            y_pred = self.model.predict(X_test)
+
+            # Evaluate the model
+            mse = mean_squared_error(y_test, y_pred)
+            mse_values.append(mse)
+
+        # SAVE THE EVOLUTION OF THE MSE THROUGHOUT THE TRAINING 
+        plt.figure(figsize=(10, 6))
+        plt.plot(range(epochs), mse_values, marker='o', linestyle='-')
+        plt.title(f"Evolution of MSE During Training. Final MSE = {mse:.4f}")
+        plt.xlabel('Epoch')
+        plt.ylabel('Mean Squared Error')
+        plt.grid(True)
+        plt.savefig(os.path.join("app", "NeuralNetwork", "graph.png"))
+
+        print(f"Final epoch MSE: {mse:.4f}")
+
+    def predict(self, input_data: pd.DataFrame) -> float:
+        # scale the input using the scaler used during training
+        df_used_for_scaling = input_data[
+            [
+                col
+                for col in input_data.columns
+                if col
+                not in [
+                    "Gender_Male",
+                    "Gender_Female",
+                    "Workout_Type_Strength",
+                    "Workout_Type_Yoga",
+                    "Workout_Type_HIIT",
+                    "Workout_Type_Cardio",
+                ]
+            ]
+        ]
+        scaled_input = self.scaler.transform(
+            input_data[[col for col in df_used_for_scaling.columns]]
+        )
+        input_data[df_used_for_scaling.columns] = scaled_input.copy()
+
+        # keep only the required features for the prediction
+        input_data = input_data.drop(self.features_to_remove, axis=1, errors="ignore")
+
+        input_data = input_data[self.numeric_columns]
+
+        print("Prediction using the following input : ")
+        print(input_data.to_csv())
+
+        water_intake = self.model.predict(input_data)
+        return water_intake

SVM/SVM_C.py

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+import os
+import pickle
+import pandas as pd
+
+class SVM_Classifier:
+    def __init__(self):
+        
+        self.weight = 70  # default weight in kg
+        self.height = 1.75  # default height in m
+        self.gender = "Male"  # default gender
+        self.duration = 1.0  # default duration in hours
+        self.fat = 25  # default fat percentage
+        self.freq = 3  # default workouts per week
+        self.experience = 1  # default experience level
+        self.workout = "Cardio"  # default workout type
+
+        # Add debug info dictionary
+        self.debug_info = {}
+        
+        # Load the model and required data
+        try:
+            # Load the SVM model
+            model_file = os.path.join("app", "data", "svm_model.pkl")
+            with open(model_file, 'rb') as f:
+                self.svm_model = pickle.load(f)
+            
+            # Load the column names
+            cols_file = os.path.join("app", "data", "column_names.csv")
+            with open(cols_file, 'r') as f:
+                self.column_names = [line.strip() for line in f]
+            
+            # Load normalization parameters
+            mean_file = os.path.join("app", "data", "SVM_train_mean.csv")
+            self.df_mean = pd.read_csv(mean_file, index_col=0)
+            
+            std_file = os.path.join("app", "data", "SVM_train_std.csv")
+            self.df_std = pd.read_csv(std_file, index_col=0)
+            
+        except Exception as e:
+            print(f"Error loading model files: {str(e)}")
+            raise
+
+    def make_prediction(self):
+        try:
+            num = [self.weight, self.height, self.duration, self.fat, self.freq, self.experience]
+            self.debug_info['original_values'] = dict(zip(
+                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'], 
+                num
+            ))
+
+            m_norm = self.df_mean.values.flatten().tolist()
+            s_norm = self.df_std.values.flatten().tolist()
+            self.debug_info['normalization'] = {
+                'means': m_norm,
+                'stds': s_norm
+            }
+
+            norm = [(x-y)/z for x, y, z in zip(num, m_norm, s_norm)]
+            self.debug_info['normalized_values'] = dict(zip(
+                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'],
+                norm
+            ))
+
+            if self.gender == 'Female':
+                norm.extend([1,0])
+                self.debug_info['gender_encoding'] = 'Female: [1, 0]'
+            else:
+                norm.extend([0,1])
+                self.debug_info['gender_encoding'] = 'Male: [0, 1]'
+
+            # Add one-hot encoded workout type
+            workout_encoding = {
+                'Cardio': [1, 0, 0, 0],
+                'HIIT': [0, 1, 0, 0],
+                'Strength': [0, 0, 1, 0],
+                'Yoga': [0, 0, 0, 1]
+            }
+            norm.extend(workout_encoding[self.workout])
+            self.debug_info['workout_encoding'] = f'{self.workout}: {workout_encoding[self.workout]}'
+
+            X = pd.DataFrame([norm],columns=self.column_names)
+            self.debug_info['final_feature_vector'] = X.to_dict('records')[0]
+
+            prediction = self.svm_model.predict(X)
+            self.debug_info['prediction'] = prediction[0]
+
+            return prediction[0]
+
+        except Exception as e:
+            self.debug_info['error'] = str(e)
+            return f"Error: {str(e)}"
+
+    def get_debug_info(self):
+        """Returns formatted debug information"""
+        debug_text = "=== DEBUG INFORMATION ===\n\n"
+        
+        # Original values
+        debug_text += "Original Values:\n"
+        for key, value in self.debug_info['original_values'].items():
+            debug_text += f"{key}: {value}\n"
+        
+        # Normalization parameters
+        debug_text += "\nNormalization Parameters:\n"
+        for i, (mean, std) in enumerate(zip(
+            self.debug_info['normalization']['means'],
+            self.debug_info['normalization']['stds']
+        )):
+            debug_text += f"Feature {i}: mean={mean:.4f}, std={std:.4f}\n"
+        
+        # Normalized values
+        debug_text += "\nNormalized Values:\n"
+        for key, value in self.debug_info['normalized_values'].items():
+            debug_text += f"{key}: {value:.4f}\n"
+        
+        # Encodings
+        debug_text += f"\nGender Encoding: {self.debug_info['gender_encoding']}\n"
+        debug_text += f"Workout Encoding: {self.debug_info['workout_encoding']}\n"
+
+        # Final vector
+        debug_text += f"\nVector: {self.debug_info['final_feature_vector']}\n"
+        
+        # Final prediction
+        debug_text += f"\nFinal Prediction: {self.debug_info['prediction']}\n"
+        
+        return debug_text

SVM/SVM_R.py

@@ -0,0 +1,130 @@
+import os
+import pickle
+import pandas as pd
+
+class SVM_Regressor:
+    def __init__(self):
+        
+        self.weight = 70  # default weight in kg
+        self.height = 1.75  # default height in m
+        self.gender = "Male"  # default gender
+        self.duration = 1.0  # default duration in hours
+        self.fat = 25  # default fat percentage
+        self.freq = 3  # default workouts per week
+        self.experience = 1  # default experience level
+        self.workout = "Cardio"  # default workout type
+
+        # Add debug info dictionary
+        self.debug_info = {}
+        
+        # Load the model and required data
+        try:
+            # Load the SVM model
+            model_file = os.path.join("app", "data", "svr_model.pkl")
+            with open(model_file, 'rb') as f:
+                self.svm_model = pickle.load(f)
+            
+            # Load the column names
+            cols_file = os.path.join("app", "data", "column_names.csv")
+            with open(cols_file, 'r') as f:
+                self.column_names = [line.strip() for line in f]
+            
+            # Load normalization parameters
+            Xmean_file = os.path.join("app", "data", "SVR_train_mean.csv")
+            self.df_mean = pd.read_csv(Xmean_file, index_col=0)
+            
+            Xstd_file = os.path.join("app", "data", "SVR_train_std.csv")
+            self.df_std = pd.read_csv(Xstd_file, index_col=0)
+
+            ynorm_file = os.path.join("app", "data", "svr_y_norms.csv")
+            df_ynorm = pd.read_csv(ynorm_file, index_col=False)
+            self.y_mean = df_ynorm['y_mean'].iloc[0]
+            self.y_std = df_ynorm['y_std'].iloc[0]
+            
+        except Exception as e:
+            print(f"Error loading model files: {str(e)}")
+            raise
+
+    def make_prediction(self):
+        try:
+            num = [self.weight, self.height, self.duration, self.fat, self.freq, self.experience]
+            self.debug_info['original_values'] = dict(zip(
+                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'], 
+                num
+            ))
+
+            m_norm = self.df_mean.values.flatten().tolist()
+            s_norm = self.df_std.values.flatten().tolist()
+            self.debug_info['normalization'] = {
+                'means': m_norm,
+                'stds': s_norm
+            }
+
+            norm = [(x-y)/z for x, y, z in zip(num, m_norm, s_norm)]
+            self.debug_info['normalized_values'] = dict(zip(
+                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'],
+                norm
+            ))
+
+            if self.gender == 'Female':
+                norm.extend([1,0])
+                self.debug_info['gender_encoding'] = 'Female: [1, 0]'
+            else:
+                norm.extend([0,1])
+                self.debug_info['gender_encoding'] = 'Male: [0, 1]'
+
+            # Add one-hot encoded workout type
+            workout_encoding = {
+                'Cardio': [1, 0, 0, 0],
+                'HIIT': [0, 1, 0, 0],
+                'Strength': [0, 0, 1, 0],
+                'Yoga': [0, 0, 0, 1]
+            }
+            norm.extend(workout_encoding[self.workout])
+            self.debug_info['workout_encoding'] = f'{self.workout}: {workout_encoding[self.workout]}'
+
+            X = pd.DataFrame([norm],columns=self.column_names)
+            self.debug_info['final_feature_vector'] = X.to_dict('records')[0]
+
+            prediction = self.svm_model.predict(X)
+            self.debug_info['prediction'] = prediction[0]
+
+            return f"""It is recommended to take {(prediction[0]*self.y_std + self.y_mean):.2f} litres of water for this session."""
+
+        except Exception as e:
+            self.debug_info['error'] = str(e)
+            return f"Error: {str(e)}"
+
+    def get_debug_info(self):
+        """Returns formatted debug information"""
+        debug_text = "=== DEBUG INFORMATION ===\n\n"
+        
+        # Original values
+        debug_text += "Original Values:\n"
+        for key, value in self.debug_info['original_values'].items():
+            debug_text += f"{key}: {value}\n"
+        
+        # Normalization parameters
+        debug_text += "\nNormalization Parameters:\n"
+        for i, (mean, std) in enumerate(zip(
+            self.debug_info['normalization']['means'],
+            self.debug_info['normalization']['stds']
+        )):
+            debug_text += f"Feature {i}: mean={mean:.4f}, std={std:.4f}\n"
+        
+        # Normalized values
+        debug_text += "\nNormalized Values:\n"
+        for key, value in self.debug_info['normalized_values'].items():
+            debug_text += f"{key}: {value:.4f}\n"
+        
+        # Encodings
+        debug_text += f"\nGender Encoding: {self.debug_info['gender_encoding']}\n"
+        debug_text += f"Workout Encoding: {self.debug_info['workout_encoding']}\n"
+
+        # Final vector
+        debug_text += f"\nVector: {self.debug_info['final_feature_vector']}\n"
+        
+        # Final prediction
+        debug_text += f"\nFinal Prediction: {self.debug_info['prediction']}\n"
+        
+        return debug_text

data/SVM_train_mean.csv

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+,0
+Weight (kg),73.34884979702301
+Height (m),1.7188362652232745
+Session_Duration (hours),1.2282408660351827
+Fat_Percentage,25.646143437077132
+Workout_Frequency (days/week),3.2205683355886334
+Experience_Level,1.7456021650879567

data/SVM_train_std.csv

@@ -0,0 +1,7 @@
+,0
+Weight (kg),21.0623331745688
+Height (m),0.12762261858774263
+Session_Duration (hours),0.32066224057136233
+Fat_Percentage,5.79558944870914
+Workout_Frequency (days/week),0.8418647527500103
+Experience_Level,0.7063141541349356

data/SVR_train_mean.csv

@@ -0,0 +1,7 @@
+,0
+Weight (kg),73.34884979702301
+Height (m),1.7188362652232745
+Session_Duration (hours),1.2282408660351827
+Fat_Percentage,25.646143437077132
+Workout_Frequency (days/week),3.2205683355886334
+Experience_Level,1.7456021650879567

data/SVR_train_std.csv

@@ -0,0 +1,7 @@
+,0
+Weight (kg),21.0623331745688
+Height (m),0.12762261858774263
+Session_Duration (hours),0.32066224057136233
+Fat_Percentage,5.79558944870914
+Workout_Frequency (days/week),0.8418647527500103
+Experience_Level,0.7063141541349356

data/column_names.csv

@@ -0,0 +1,12 @@
+Weight (kg)
+Height (m)
+Session_Duration (hours)
+Fat_Percentage
+Workout_Frequency (days/week)
+Experience_Level
+Gender_Female
+Gender_Male
+Workout_Type_Cardio
+Workout_Type_HIIT
+Workout_Type_Strength
+Workout_Type_Yoga

data/gym_members_exercise_tracking.csv

@@ -0,0 +1,974 @@
+Age,Gender,Weight (kg),Height (m),Max_BPM,Avg_BPM,Resting_BPM,Session_Duration (hours),Calories_Burned,Workout_Type,Fat_Percentage,Water_Intake (liters),Workout_Frequency (days/week),Experience_Level,BMI
+56,Male,88.3,1.71,180,157,60,1.69,1313.0,Yoga,12.6,3.5,4,3,30.2
+46,Female,74.9,1.53,179,151,66,1.3,883.0,HIIT,33.9,2.1,4,2,32.0
+32,Female,68.1,1.66,167,122,54,1.11,677.0,Cardio,33.4,2.3,4,2,24.71
+25,Male,53.2,1.7,190,164,56,0.59,532.0,Strength,28.8,2.1,3,1,18.41
+38,Male,46.1,1.79,188,158,68,0.64,556.0,Strength,29.2,2.8,3,1,14.39
+56,Female,58.0,1.68,168,156,74,1.59,1116.0,HIIT,15.5,2.7,5,3,20.55
+36,Male,70.3,1.72,174,169,73,1.49,1385.0,Cardio,21.3,2.3,3,2,23.76
+40,Female,69.7,1.51,189,141,64,1.27,895.0,Cardio,30.6,1.9,3,2,30.57
+28,Male,121.7,1.94,185,127,52,1.03,719.0,Strength,28.9,2.6,4,2,32.34
+28,Male,101.8,1.84,169,136,64,1.08,808.0,Cardio,29.7,2.7,3,1,30.07
+41,Male,120.8,1.67,188,146,54,0.82,593.0,HIIT,20.5,3.0,2,1,43.31
+53,Male,51.7,1.7,175,152,72,1.15,865.0,HIIT,23.6,3.5,3,2,17.89
+57,Male,112.5,1.61,195,165,61,1.24,1013.0,Cardio,22.1,2.7,3,2,43.4
+41,Male,94.5,2.0,179,136,69,1.18,794.0,HIIT,27.6,3.7,3,1,23.62
+20,Male,117.7,1.81,196,161,54,1.35,1195.0,Yoga,26.4,3.3,3,2,35.93
+39,Female,42.5,1.75,181,131,52,1.13,740.0,Strength,26.2,2.1,2,1,13.88
+19,Female,64.0,1.53,166,167,58,1.33,1111.0,HIIT,29.8,2.3,3,2,27.34
+41,Female,43.8,1.77,182,165,58,1.19,884.0,Cardio,31.9,1.6,3,1,13.98
+47,Female,66.8,1.75,199,146,56,1.13,742.0,Strength,32.8,2.5,3,2,21.81
+55,Female,75.2,1.67,188,167,51,1.37,1030.0,HIIT,25.2,2.2,2,1,26.96
+19,Male,89.0,1.77,175,127,72,1.5,1048.0,Strength,28.9,3.7,4,2,28.41
+38,Male,71.9,1.77,197,142,72,1.12,875.0,Cardio,25.7,3.1,2,1,22.95
+50,Female,71.0,1.68,187,161,70,1.17,848.0,Yoga,33.1,2.5,2,1,25.16
+29,Male,120.9,1.78,197,168,65,0.78,721.0,Yoga,28.1,3.4,2,1,38.16
+39,Female,64.3,1.69,190,148,58,1.25,925.0,HIIT,26.9,1.9,4,2,22.51
+42,Female,63.7,1.71,173,169,62,1.42,1080.0,Yoga,26.1,1.8,3,2,21.78
+44,Male,65.2,1.8,192,139,68,0.73,502.0,HIIT,27.2,2.7,2,1,20.12
+59,Male,53.9,1.75,168,135,69,1.48,989.0,Strength,21.6,2.8,4,2,17.6
+45,Male,84.9,1.86,186,136,66,1.64,1104.0,HIIT,14.2,3.5,5,3,24.54
+33,Female,78.0,1.68,183,135,55,1.29,871.0,HIIT,32.3,1.8,3,2,27.64
+32,Male,108.2,1.8,172,138,53,1.27,964.0,HIIT,28.4,3.5,3,1,33.4
+20,Female,65.4,1.52,185,127,50,1.03,654.0,Yoga,28.0,2.2,4,2,28.31
+54,Female,50.2,1.61,188,157,67,1.48,1046.0,HIIT,28.2,2.7,2,1,19.37
+24,Female,58.9,1.51,187,157,68,1.04,816.0,Cardio,31.7,2.5,2,1,25.83
+38,Male,81.4,1.71,187,148,58,1.52,1237.0,HIIT,10.2,3.5,5,3,27.84
+26,Male,127.6,1.73,167,160,62,1.32,1162.0,Strength,27.3,2.9,3,1,42.63
+56,Female,59.3,1.56,182,155,57,1.26,879.0,Cardio,33.4,2.6,4,2,24.37
+35,Male,96.9,1.71,188,145,72,1.34,1069.0,Strength,24.2,2.9,3,2,33.14
+21,Male,62.6,1.81,164,150,62,1.3,1072.0,Strength,27.6,3.5,4,2,19.11
+42,Male,45.5,1.6,166,163,64,1.31,1057.0,Strength,21.7,2.6,3,2,17.77
+31,Female,48.8,1.51,195,131,60,1.48,969.0,Cardio,28.2,2.0,2,1,21.4
+26,Female,44.3,1.6,186,136,61,1.08,734.0,Cardio,34.7,2.0,3,1,17.3
+43,Male,113.2,1.83,161,134,63,1.45,962.0,Yoga,29.9,2.9,4,2,33.8
+19,Female,60.5,1.59,184,128,53,1.14,730.0,Cardio,30.1,2.2,2,1,23.93
+37,Male,124.2,1.76,168,158,50,1.5,1304.0,HIIT,29.3,2.8,2,1,40.1
+45,Male,52.4,1.85,186,161,73,1.2,956.0,Strength,25.2,2.5,3,1,15.31
+24,Male,54.7,1.74,160,167,53,0.84,772.0,Yoga,22.9,3.6,3,1,18.07
+25,Male,88.1,1.95,182,131,68,1.41,1016.0,Cardio,22.7,3.0,2,1,23.17
+52,Female,59.7,1.71,169,151,58,1.23,836.0,Strength,34.3,2.0,3,1,20.42
+31,Female,79.7,1.54,184,144,59,1.03,742.0,Cardio,26.7,2.3,4,2,33.61
+34,Female,51.0,1.62,162,158,53,1.42,1122.0,Yoga,30.8,2.4,3,2,19.43
+53,Male,84.2,1.76,165,137,69,1.67,1133.0,Cardio,12.8,3.5,5,3,27.18
+57,Male,122.1,1.89,165,134,58,1.13,750.0,Cardio,27.4,3.6,3,1,34.18
+21,Male,96.7,1.72,161,151,62,1.26,1046.0,Strength,25.0,2.8,3,1,32.69
+19,Female,73.0,1.79,177,125,61,0.81,506.0,HIIT,27.7,1.6,3,1,22.78
+23,Male,114.8,1.6,182,163,60,1.47,1318.0,Cardio,21.7,2.7,4,2,44.84
+59,Female,65.5,1.52,161,162,61,1.4,1021.0,Yoga,31.2,2.1,3,2,28.35
+21,Female,50.3,1.52,171,154,67,1.08,832.0,Cardio,32.2,2.6,3,1,21.77
+46,Female,61.0,1.71,181,153,61,1.67,1150.0,Cardio,17.8,2.7,4,3,20.86
+35,Female,44.6,1.62,196,157,65,0.56,440.0,HIIT,32.4,2.0,2,1,16.99
+43,Female,58.2,1.61,179,124,54,1.04,580.0,Strength,26.4,2.6,3,2,22.45
+51,Female,44.8,1.63,198,137,69,1.08,666.0,Yoga,33.8,2.1,4,2,16.86
+27,Male,87.5,1.63,183,135,74,1.75,1299.0,Strength,12.9,3.5,5,3,32.93
+53,Male,51.8,1.74,179,127,61,1.23,773.0,Strength,21.3,3.7,3,2,17.11
+31,Female,64.4,1.7,160,144,70,1.97,1418.0,Strength,17.3,2.7,5,3,22.28
+48,Female,67.1,1.61,160,151,74,0.62,421.0,Cardio,33.4,1.5,2,1,25.89
+32,Male,85.9,1.6,176,145,60,1.99,1587.0,HIIT,14.5,3.5,5,3,33.55
+25,Female,65.4,1.77,160,141,58,0.89,627.0,Cardio,27.6,2.6,3,1,20.88
+31,Female,61.1,1.76,189,150,67,1.81,1358.0,Yoga,18.6,2.7,4,3,19.72
+40,Male,106.5,1.65,162,162,74,1.32,1176.0,HIIT,21.0,3.6,4,2,39.12
+57,Female,64.0,1.52,170,129,52,1.73,1004.0,Strength,15.2,2.7,5,3,27.7
+38,Female,61.4,1.54,183,131,69,1.47,963.0,HIIT,30.6,2.7,3,2,25.89
+33,Female,40.5,1.63,181,147,66,1.07,786.0,Yoga,31.9,1.5,4,2,15.24
+35,Male,70.1,1.79,185,125,63,1.08,743.0,Cardio,28.0,2.3,3,2,21.88
+41,Male,94.1,1.62,172,139,50,1.13,777.0,Cardio,23.4,3.1,4,2,35.86
+43,Male,55.5,1.82,160,124,66,1.08,663.0,Strength,20.1,2.0,3,1,16.76
+42,Female,53.7,1.72,183,142,74,1.37,875.0,Cardio,29.8,1.6,3,1,18.15
+58,Female,66.1,1.75,169,128,57,0.63,363.0,Cardio,25.2,1.8,2,1,21.58
+46,Male,103.9,1.77,194,148,54,1.18,864.0,Yoga,20.9,2.9,2,1,33.16
+32,Female,61.7,1.58,183,135,54,1.87,1262.0,HIIT,16.6,2.7,5,3,24.72
+18,Female,52.3,1.74,187,165,58,1.2,990.0,Cardio,29.4,2.2,2,1,17.27
+42,Female,59.5,1.74,195,141,52,1.78,1129.0,Yoga,16.4,2.7,5,3,19.65
+24,Female,72.9,1.76,175,127,68,0.86,546.0,Yoga,30.8,1.5,3,1,23.53
+26,Female,76.7,1.63,199,139,72,1.08,751.0,Strength,25.6,1.7,2,1,28.87
+41,Male,72.1,1.83,175,133,58,1.13,744.0,Yoga,21.4,2.8,3,2,21.53
+18,Female,54.8,1.68,176,158,71,1.27,1003.0,Strength,32.0,2.2,4,2,19.42
+25,Male,105.0,1.88,174,156,67,1.34,1150.0,Cardio,21.4,2.3,3,2,29.71
+41,Female,55.0,1.55,175,169,65,1.45,1103.0,Strength,31.4,2.0,4,2,22.89
+28,Male,81.6,1.66,174,153,50,1.09,917.0,Yoga,20.5,3.5,4,2,29.61
+34,Female,75.6,1.6,193,130,51,0.64,416.0,HIIT,29.1,2.7,2,1,29.53
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+52,Female,61.5,1.69,160,152,63,1.8,1231.0,Cardio,15.5,2.7,4,3,21.53
+52,Male,74.0,1.63,174,150,50,1.26,936.0,Cardio,25.7,3.7,3,2,27.85
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+22,Male,102.6,1.84,163,153,65,1.25,1052.0,Strength,20.5,2.7,3,2,30.3
+59,Female,54.6,1.57,164,169,64,0.58,441.0,Cardio,29.2,2.3,3,1,22.15
+56,Male,129.0,1.78,194,126,64,1.29,805.0,Yoga,27.1,2.7,2,1,40.71
+58,Male,103.5,1.66,172,168,61,1.33,1106.0,Cardio,24.6,3.1,3,1,37.56
+45,Female,59.4,1.51,169,142,65,1.15,735.0,Strength,33.1,1.9,4,2,26.05
+24,Female,60.1,1.79,170,165,63,1.97,1625.0,Cardio,15.9,2.7,4,3,18.76
+26,Male,77.7,1.62,177,126,73,1.16,804.0,Yoga,29.6,3.0,3,2,29.61
+25,Female,41.2,1.62,193,144,64,1.49,1073.0,Yoga,28.9,2.1,4,2,15.7
+29,Male,58.6,1.61,198,122,72,1.2,805.0,Yoga,23.4,2.0,3,2,22.61
+51,Male,109.3,1.78,182,157,64,0.54,420.0,Strength,28.7,3.3,3,1,34.5
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+40,Female,56.2,1.79,198,153,57,1.79,1369.0,Yoga,17.6,2.7,5,3,17.54
+41,Male,71.7,1.96,162,168,51,0.51,424.0,Yoga,27.3,2.5,2,1,18.66
+54,Male,86.0,1.93,171,163,51,1.74,1404.0,Strength,12.9,3.5,5,3,23.09
+52,Female,79.4,1.59,166,163,59,1.39,1020.0,Strength,26.6,1.5,3,2,31.41
+57,Female,78.4,1.62,185,149,52,1.01,677.0,Strength,31.7,2.0,4,2,29.87
+39,Female,55.4,1.6,192,136,72,1.44,979.0,HIIT,26.3,1.6,3,2,21.64
+44,Female,61.7,1.55,196,129,66,1.41,819.0,Strength,33.1,2.6,4,2,25.68
+52,Male,85.5,1.82,174,154,57,1.78,1357.0,Yoga,14.9,3.5,4,3,25.81
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+52,Male,82.4,1.72,166,137,53,1.01,685.0,Strength,24.4,2.7,4,2,27.85
+54,Male,67.0,1.68,191,164,63,0.55,446.0,Yoga,20.5,2.8,3,1,23.74
+31,Female,42.3,1.6,163,163,70,1.09,888.0,Cardio,25.1,1.9,3,2,16.52
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+43,Male,82.7,1.85,187,142,50,1.22,858.0,Yoga,28.2,3.1,4,2,24.16
+31,Male,86.6,1.76,172,151,66,1.29,1071.0,Strength,28.9,3.3,3,2,27.96
+56,Male,129.5,1.95,160,129,66,1.0,639.0,Strength,25.2,3.5,2,1,34.06
+44,Male,98.0,1.83,194,130,56,1.28,824.0,Cardio,25.7,3.5,4,2,29.26
+26,Male,86.2,1.68,189,157,72,1.97,1701.0,Strength,13.3,3.5,5,3,30.54
+32,Male,78.6,1.62,195,125,69,1.28,880.0,Yoga,29.6,3.1,3,1,29.95
+32,Female,59.0,1.66,184,160,64,0.51,408.0,Cardio,31.0,1.8,2,1,21.41
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+41,Male,64.8,1.71,164,131,54,1.2,778.0,Strength,22.5,2.8,3,2,22.16
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+56,Female,52.9,1.64,169,155,53,1.1,767.0,Cardio,27.4,2.3,3,2,19.67
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+18,Male,68.1,1.8,174,153,50,1.1,926.0,Yoga,25.4,3.3,4,2,21.02
+36,Male,69.2,1.65,165,126,65,1.23,852.0,Strength,27.2,2.7,3,1,25.42
+19,Female,65.2,1.52,188,130,71,1.23,800.0,Cardio,30.6,2.4,3,1,28.22
+43,Male,108.6,1.73,174,150,66,1.33,988.0,Cardio,28.5,2.2,2,1,36.29
+49,Female,40.0,1.65,188,148,74,1.03,686.0,Yoga,34.3,2.5,3,2,14.69
+23,Female,41.9,1.58,163,132,67,0.56,370.0,HIIT,26.8,1.8,3,1,16.78
+49,Female,68.3,1.52,177,150,60,1.45,979.0,Strength,29.4,1.8,4,2,29.56
+21,Male,88.4,1.6,198,121,67,1.71,1138.0,HIIT,12.0,3.5,4,3,34.53
+28,Male,66.0,1.9,175,158,56,1.18,1025.0,HIIT,24.1,3.0,3,1,18.28
+34,Male,71.9,1.64,170,134,67,1.26,929.0,Cardio,21.3,2.1,4,2,26.73
+55,Male,75.6,1.71,173,130,57,1.03,663.0,Strength,28.1,2.0,4,2,25.85
+41,Female,63.4,1.59,162,153,55,1.84,1267.0,HIIT,15.2,2.7,5,3,25.08
+22,Female,71.6,1.77,197,158,56,1.49,1177.0,Yoga,30.3,1.8,3,1,22.85
+51,Female,57.4,1.77,177,123,72,1.08,598.0,Yoga,31.6,2.1,4,2,18.32
+23,Male,83.8,1.99,179,130,71,1.79,1280.0,Cardio,11.0,3.5,4,3,21.16
+39,Female,55.9,1.71,184,148,59,1.95,1443.0,Yoga,19.5,2.7,5,3,19.12
+28,Male,87.0,1.76,197,121,60,1.2,799.0,Yoga,28.4,3.5,3,2,28.09
+33,Female,65.3,1.71,172,135,74,1.18,796.0,HIIT,29.7,2.5,4,2,22.33
+50,Male,45.4,1.67,163,158,62,1.37,1071.0,Yoga,24.1,2.8,4,2,16.28
+26,Male,87.1,1.87,173,132,58,1.71,1241.0,Yoga,11.2,3.5,4,3,24.91
+23,Female,78.6,1.73,192,156,56,1.47,1147.0,HIIT,32.3,1.5,4,2,26.26
+33,Female,78.0,1.71,187,136,58,1.08,734.0,Cardio,26.3,2.1,3,1,26.67
+46,Female,58.0,1.53,196,140,62,1.83,1153.0,Cardio,18.4,2.7,4,3,24.78
+20,Female,71.3,1.69,167,163,52,1.28,1043.0,Strength,33.4,2.3,3,2,24.96
+37,Male,49.1,1.74,193,138,52,1.11,842.0,Yoga,22.3,2.6,3,2,16.22
+53,Female,78.3,1.65,164,126,50,1.24,703.0,HIIT,33.7,1.6,3,1,28.76
+36,Female,57.3,1.64,164,157,70,1.13,887.0,Strength,25.3,1.8,4,2,21.3
+43,Female,48.4,1.61,190,127,69,1.28,732.0,Cardio,34.1,2.4,2,1,18.67
+20,Male,76.4,1.62,168,132,51,1.35,980.0,Cardio,21.2,2.6,4,2,29.11
+36,Male,110.2,1.77,191,156,74,1.19,1021.0,HIIT,26.5,3.4,2,1,35.18
+37,Female,72.9,1.76,192,121,52,1.21,732.0,Strength,27.5,1.9,3,2,23.53
+49,Male,82.1,1.86,162,160,64,1.87,1481.0,Strength,14.2,3.5,4,3,23.73
+24,Female,58.9,1.76,189,130,73,0.63,410.0,HIIT,30.1,1.7,3,1,19.01
+58,Male,82.2,1.87,171,143,55,1.57,1111.0,HIIT,10.7,3.5,4,3,23.51
+50,Male,96.7,1.72,188,125,62,1.46,903.0,Strength,25.7,2.7,3,2,32.69
+57,Male,81.4,1.86,162,145,70,1.8,1292.0,Cardio,12.0,3.5,5,3,23.53
+56,Male,107.8,1.73,185,126,50,1.09,680.0,Yoga,26.6,2.8,3,2,36.02
+35,Female,65.8,1.62,189,158,69,1.0,790.0,Cardio,33.8,1.5,4,2,25.07
+57,Female,68.5,1.56,168,138,68,1.45,900.0,HIIT,29.1,1.6,4,2,28.15
+18,Female,63.9,1.59,185,125,50,1.87,1169.0,Strength,16.4,2.7,4,3,25.28
+28,Male,76.3,1.62,161,162,70,1.16,1034.0,Strength,20.1,2.3,3,2,29.07
+45,Female,44.5,1.65,162,148,71,1.44,959.0,Yoga,30.7,1.6,3,1,16.35
+42,Female,50.7,1.75,163,162,51,1.29,940.0,Strength,29.0,1.9,3,2,16.56
+40,Female,55.1,1.61,184,158,64,1.55,1224.0,Yoga,16.9,2.7,4,3,21.26
+48,Male,73.7,1.61,172,121,63,1.36,815.0,Cardio,21.8,3.6,4,2,28.43
+47,Male,55.6,1.77,198,138,68,1.2,820.0,Cardio,24.3,2.9,4,2,17.75
+59,Male,86.9,1.76,194,123,60,1.13,688.0,Strength,29.8,3.1,3,2,28.05
+52,Female,60.3,1.68,182,125,64,1.6,900.0,Strength,16.5,2.7,4,3,21.36
+24,Female,40.4,1.64,168,168,55,0.99,832.0,HIIT,31.0,1.9,2,1,15.02
+33,Female,78.5,1.66,161,156,69,1.29,1006.0,HIIT,32.8,1.9,4,2,28.49
+43,Male,102.6,1.72,190,147,58,0.82,597.0,Strength,27.5,2.0,2,1,34.68
+19,Male,110.8,1.91,161,149,54,1.31,1074.0,Strength,23.3,2.9,2,1,30.37
+18,Female,60.9,1.79,191,126,69,1.11,699.0,Cardio,31.1,2.1,2,1,19.01
+29,Male,55.7,1.61,177,128,51,1.41,993.0,Strength,28.7,2.3,3,1,21.49
+22,Male,110.5,1.92,172,144,55,1.16,919.0,HIIT,20.2,3.3,3,2,29.98
+54,Male,55.2,1.71,164,152,67,0.77,579.0,Cardio,24.4,2.8,2,1,18.88
+49,Male,46.5,1.72,169,120,72,1.4,832.0,Yoga,22.2,2.6,4,2,15.72
+26,Female,70.9,1.66,184,125,68,0.81,506.0,HIIT,28.9,1.7,3,1,25.73
+58,Male,74.6,1.75,193,137,58,1.09,739.0,HIIT,24.2,2.9,2,1,24.36
+52,Male,45.1,1.72,196,129,70,0.93,594.0,Cardio,27.5,3.3,2,1,15.24
+36,Male,99.9,1.99,189,147,73,1.33,1075.0,Yoga,20.9,2.5,3,1,25.23
+33,Male,117.0,1.61,166,130,55,1.04,744.0,HIIT,25.1,2.4,3,2,45.14
+20,Female,75.9,1.73,165,152,63,1.39,1056.0,Strength,30.6,2.1,4,2,25.36
+37,Female,71.2,1.64,162,120,73,1.11,666.0,HIIT,29.9,1.7,3,2,26.47
+41,Male,78.8,1.72,172,132,62,1.01,660.0,Cardio,27.3,3.1,4,2,26.64
+50,Male,50.6,1.69,162,156,60,1.31,1012.0,Cardio,23.8,3.0,2,1,17.72
+41,Male,82.5,1.79,185,139,61,1.69,1163.0,Cardio,14.7,3.5,4,3,25.75
+28,Male,108.5,1.79,183,120,68,1.33,878.0,HIIT,24.8,2.1,4,2,33.86
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+53,Male,125.1,1.91,171,137,70,0.52,353.0,Strength,28.3,3.1,2,1,34.29
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+57,Male,126.8,1.63,161,133,73,0.96,632.0,Yoga,20.8,2.1,2,1,47.72
+37,Female,54.6,1.57,170,150,62,1.31,982.0,Strength,32.1,2.4,3,1,22.15
+52,Female,47.8,1.54,198,146,63,1.34,880.0,Cardio,29.8,1.9,3,2,20.16
+42,Male,97.9,1.91,193,136,73,1.01,680.0,Strength,29.0,2.8,3,2,26.84
+52,Female,63.1,1.54,185,165,67,1.36,1010.0,Strength,34.8,1.8,3,2,26.61
+42,Female,72.4,1.69,177,148,61,1.07,713.0,Yoga,29.0,2.3,3,2,25.35
+46,Female,58.9,1.7,180,155,53,1.52,1060.0,Strength,17.1,2.7,4,3,20.38
+35,Female,50.4,1.6,195,156,59,1.03,803.0,Cardio,29.1,1.9,4,2,19.69
+35,Female,64.2,1.59,199,145,66,1.63,1182.0,Yoga,16.6,2.7,5,3,25.39
+19,Female,58.8,1.77,181,136,53,1.4,952.0,HIIT,32.8,2.7,2,1,18.77
+52,Male,105.2,1.62,189,141,61,1.36,949.0,HIIT,27.6,3.2,3,1,40.09
+33,Female,45.9,1.5,189,144,62,0.92,662.0,Yoga,29.8,1.7,3,1,20.4
+58,Female,65.2,1.63,167,121,53,1.12,610.0,HIIT,25.4,2.0,3,2,24.54
+53,Female,56.3,1.79,193,141,59,1.52,964.0,Strength,16.5,2.7,5,3,17.57
+50,Male,90.0,1.7,168,124,74,0.81,497.0,Cardio,22.9,2.7,2,1,31.14
+21,Female,60.0,1.7,179,123,52,1.97,1212.0,Strength,19.1,2.7,5,3,20.76
+50,Male,51.5,1.71,199,155,64,1.48,1136.0,Yoga,20.9,2.9,2,1,17.61
+31,Female,63.1,1.64,184,125,51,1.54,962.0,Cardio,19.0,2.7,4,3,23.46
+38,Male,91.5,1.77,178,140,74,0.8,616.0,Yoga,26.7,3.4,2,1,29.21
+37,Male,48.7,1.67,164,132,74,1.39,1009.0,Yoga,27.3,3.0,2,1,17.46
+25,Female,57.8,1.56,170,156,66,1.35,1053.0,Yoga,29.3,1.6,3,2,23.75
+24,Female,42.0,1.75,174,144,55,0.83,598.0,Strength,31.8,2.3,3,1,13.71
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+34,Female,74.2,1.57,178,158,52,1.47,1161.0,Cardio,32.7,2.4,3,1,30.1
+50,Male,89.6,1.68,182,139,74,1.79,1232.0,Cardio,12.2,3.5,5,3,31.75
+29,Male,125.9,2.0,186,152,67,1.36,1137.0,Strength,24.7,3.1,4,2,31.48
+39,Male,105.5,1.99,199,133,71,0.81,593.0,HIIT,26.4,2.5,2,1,26.64
+39,Male,93.4,1.7,191,155,50,1.1,938.0,Strength,21.1,2.5,4,2,32.32
+47,Male,82.1,1.84,185,136,65,1.37,922.0,Strength,24.4,3.4,4,2,24.25
+55,Female,42.9,1.69,186,140,56,0.9,567.0,Yoga,34.7,2.4,2,1,15.02
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+44,Male,89.3,1.99,192,135,58,1.4,936.0,HIIT,26.3,2.0,4,2,22.55
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+42,Female,57.5,1.67,192,149,55,1.14,764.0,Yoga,34.1,2.4,2,1,20.62
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+32,Male,105.3,1.67,184,143,72,0.57,448.0,Cardio,26.9,2.9,3,1,37.76
+45,Female,71.2,1.68,172,144,63,1.37,888.0,Strength,34.1,1.6,3,1,25.23
+51,Female,78.1,1.51,193,134,74,0.92,555.0,Strength,31.1,2.6,3,1,34.25
+19,Female,50.6,1.68,193,145,64,1.21,877.0,Yoga,30.6,2.1,3,2,17.93
+49,Male,70.2,1.89,194,127,51,0.7,440.0,Yoga,29.6,2.6,3,1,19.65
+40,Female,62.6,1.53,197,143,73,1.93,1380.0,Cardio,18.1,2.7,5,3,26.74
+39,Female,60.6,1.65,162,167,63,0.92,768.0,HIIT,27.6,1.7,3,1,22.26
+42,Male,76.4,1.97,165,157,65,1.14,886.0,HIIT,22.9,2.6,3,2,19.69
+39,Female,63.3,1.71,178,125,56,1.88,1175.0,Strength,18.5,2.7,4,3,21.65
+39,Female,68.6,1.64,162,154,53,1.33,1024.0,Cardio,32.4,1.5,3,2,25.51
+59,Male,123.3,1.88,177,161,67,1.3,1036.0,HIIT,23.0,2.6,4,2,34.89
+23,Male,107.0,1.94,198,120,73,1.35,891.0,HIIT,23.5,2.9,2,1,28.43
+32,Male,68.3,1.92,168,165,54,1.42,1289.0,Strength,29.9,3.5,2,1,18.53
+54,Female,47.8,1.6,171,155,55,1.19,830.0,Cardio,26.0,2.3,4,2,18.67
+50,Female,58.4,1.79,164,131,56,1.6,943.0,HIIT,17.0,2.7,4,3,18.23
+25,Female,74.3,1.71,162,157,64,1.43,1123.0,Yoga,25.6,1.6,3,2,25.41
+22,Male,106.8,1.82,170,130,50,1.0,715.0,Yoga,28.2,2.3,4,2,32.24
+56,Male,125.5,1.8,189,131,73,1.26,817.0,Strength,20.9,2.7,4,2,38.73
+21,Female,55.2,1.55,163,161,53,1.37,1103.0,Strength,31.7,1.7,4,2,22.98
+23,Female,71.4,1.72,164,169,74,1.25,1056.0,Strength,25.6,1.5,2,1,24.13
+49,Male,77.7,1.61,160,158,50,1.03,806.0,Strength,26.7,3.4,3,2,29.98
+47,Male,88.4,1.6,186,126,60,1.86,1160.0,HIIT,14.2,3.5,5,3,34.53
+52,Male,104.8,1.99,187,130,67,0.93,598.0,Cardio,24.0,2.0,2,1,26.46
+57,Male,82.2,1.69,188,155,63,1.15,882.0,Strength,25.2,3.4,4,2,28.78
+33,Female,68.2,1.75,177,150,71,0.85,638.0,Strength,29.0,1.7,2,1,22.27
+30,Male,91.4,1.99,171,152,60,1.37,1145.0,HIIT,23.0,3.7,3,2,23.08
+59,Female,42.1,1.65,172,132,59,1.22,725.0,Strength,33.0,2.2,2,1,15.46
+47,Female,68.7,1.76,161,137,66,1.37,845.0,Strength,27.7,2.4,3,2,22.18
+36,Female,47.2,1.74,196,160,53,1.5,1200.0,Cardio,32.2,1.6,4,2,15.59
+34,Male,92.0,1.76,175,143,73,0.73,574.0,Yoga,22.1,3.5,3,1,29.7
+36,Male,56.2,1.95,183,128,64,1.09,767.0,Strength,22.5,3.4,4,2,14.78
+45,Male,58.4,1.72,194,150,65,1.31,973.0,HIIT,23.4,3.1,4,2,19.74
+43,Male,82.9,1.95,181,160,70,1.9,1505.0,Cardio,12.9,3.5,5,3,21.8
+54,Female,66.9,1.74,195,129,62,1.24,720.0,HIIT,29.5,2.1,2,1,22.1
+43,Male,66.3,1.85,170,121,58,1.18,707.0,Cardio,27.5,3.2,2,1,19.37
+40,Male,93.6,1.93,188,157,60,1.38,1192.0,Cardio,22.1,2.0,3,1,25.13
+26,Female,64.1,1.5,194,134,60,1.93,1293.0,Strength,15.1,2.7,5,3,28.49
+29,Male,86.7,1.62,174,122,55,1.52,1020.0,Strength,13.9,3.5,4,3,33.04
+18,Female,62.0,1.59,190,162,50,1.23,996.0,Strength,33.2,2.2,4,2,24.52
+18,Female,58.1,1.61,175,150,71,1.9,1425.0,Yoga,15.3,2.7,4,3,22.41
+51,Female,64.9,1.6,169,125,55,1.49,838.0,Yoga,31.1,2.5,4,2,25.35
+49,Female,69.3,1.62,191,122,62,1.14,626.0,HIIT,28.8,1.9,4,2,26.41
+42,Female,42.7,1.74,161,130,71,1.04,608.0,Strength,32.9,1.6,3,1,14.1
+57,Female,51.4,1.63,183,134,73,1.2,724.0,Yoga,33.7,2.0,4,2,19.35
+18,Female,67.4,1.6,182,164,71,0.6,492.0,Strength,33.5,2.4,3,1,26.33
+33,Female,55.6,1.61,161,147,54,1.77,1301.0,Yoga,18.9,2.7,4,3,21.45
+56,Male,125.3,1.86,197,129,64,1.0,639.0,Yoga,22.7,2.2,3,2,36.22
+22,Male,102.1,1.85,190,157,67,1.48,1278.0,Cardio,28.1,2.4,4,2,29.83
+39,Male,61.9,1.77,199,166,69,1.32,1205.0,Strength,25.3,2.3,3,1,19.76
+46,Male,108.8,1.64,185,169,63,1.4,1171.0,Strength,20.5,3.4,3,1,40.45
+20,Female,51.1,1.58,188,169,64,0.5,422.0,Yoga,29.6,1.6,2,1,20.47
+29,Male,81.7,1.99,162,126,54,1.45,1005.0,Strength,29.4,3.2,2,1,20.63
+43,Female,42.7,1.66,173,134,53,0.73,440.0,Yoga,33.9,1.9,2,1,15.5
+33,Male,81.8,1.99,184,137,51,1.59,1198.0,Strength,10.5,3.5,4,3,20.66
+54,Male,54.6,1.76,160,137,56,1.28,868.0,Cardio,27.4,3.3,4,2,17.63
+39,Male,107.7,1.88,162,160,59,0.72,634.0,Strength,28.1,3.6,2,1,30.47
+46,Female,56.9,1.6,185,143,65,0.88,566.0,Cardio,27.7,1.6,3,1,22.23
+31,Male,78.8,1.71,177,121,59,1.49,992.0,HIIT,24.7,2.5,4,2,26.95
+45,Female,48.2,1.64,168,159,67,1.25,894.0,Strength,32.1,2.5,4,2,17.92
+22,Female,50.7,1.59,197,139,66,1.27,883.0,Strength,29.4,1.8,3,2,20.05
+47,Male,86.6,1.99,182,153,70,1.98,1500.0,Strength,10.6,3.5,4,3,21.87
+22,Male,96.3,1.63,180,162,62,1.35,1203.0,Cardio,27.1,2.4,4,2,36.25
+29,Male,80.8,1.98,160,145,50,1.65,1316.0,Strength,14.6,3.5,5,3,20.61
+33,Female,58.0,1.53,180,126,50,1.84,1159.0,Strength,18.3,2.7,5,3,24.78
+43,Male,95.9,1.85,186,157,64,1.28,995.0,Yoga,27.7,3.4,2,1,28.02
+43,Female,61.5,1.58,165,149,50,1.86,1247.0,HIIT,16.8,2.7,5,3,24.64
+38,Female,40.6,1.79,195,161,55,0.62,499.0,Cardio,26.8,2.7,2,1,12.67
+56,Male,82.5,1.87,187,124,74,1.51,927.0,Yoga,13.2,3.5,5,3,23.59
+53,Male,105.1,1.84,176,130,62,0.69,444.0,Cardio,23.3,2.9,3,1,31.04
+50,Female,58.1,1.58,199,120,50,1.93,1042.0,Yoga,19.3,2.7,5,3,23.27
+47,Female,61.0,1.76,162,153,54,1.54,1060.0,HIIT,15.6,2.7,5,3,19.69
+54,Female,64.5,1.53,171,120,58,1.74,940.0,Yoga,17.2,2.7,4,3,27.55
+40,Male,58.2,1.98,198,138,74,1.43,1085.0,Cardio,24.0,2.5,3,2,14.85
+27,Female,69.5,1.76,171,121,66,0.94,569.0,Strength,34.4,2.0,3,1,22.44
+22,Male,62.2,1.84,161,140,65,0.51,393.0,Strength,21.5,2.3,3,1,18.37
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+51,Female,56.3,1.5,182,142,66,1.43,914.0,Cardio,27.4,2.4,3,1,25.02
+48,Male,94.5,1.7,189,140,64,1.01,700.0,Cardio,28.2,3.5,3,2,32.7
+27,Female,58.0,1.79,164,143,73,1.39,994.0,Cardio,27.1,1.5,2,1,18.1
+36,Male,84.0,1.94,168,123,50,1.8,1218.0,HIIT,11.2,3.5,5,3,22.32
+49,Male,61.0,1.87,180,131,61,1.14,739.0,Strength,21.3,3.0,4,2,17.44
+18,Female,64.8,1.53,170,141,54,1.98,1396.0,Yoga,18.6,2.7,5,3,27.68
+22,Male,45.7,1.77,164,139,73,1.04,795.0,HIIT,20.7,2.4,3,1,14.59
+21,Male,80.9,1.9,194,137,55,1.8,1356.0,Yoga,11.7,3.5,4,3,22.41
+33,Female,64.1,1.63,186,120,74,1.58,948.0,Strength,16.4,2.7,5,3,24.13
+41,Male,67.0,1.77,161,149,50,1.22,900.0,HIIT,25.6,2.6,3,2,21.39
+33,Male,106.5,1.6,183,152,74,1.4,1170.0,Strength,27.3,3.3,4,2,41.6
+19,Male,95.1,1.85,190,143,50,1.21,952.0,Yoga,25.3,2.3,4,2,27.79
+45,Female,65.5,1.57,192,133,59,0.66,395.0,Cardio,34.3,2.3,2,1,26.57
+49,Male,105.7,1.64,198,155,59,1.2,921.0,Cardio,29.5,3.1,3,2,39.3
+44,Female,48.0,1.76,189,124,52,0.88,491.0,Yoga,28.6,2.6,2,1,15.5
+37,Female,69.5,1.5,196,130,66,0.69,448.0,Cardio,32.8,2.4,2,1,30.89
+41,Male,66.1,1.69,187,143,68,0.77,545.0,Yoga,27.4,3.4,2,1,23.14
+29,Male,68.0,1.7,190,150,60,0.76,627.0,HIIT,28.9,2.9,2,1,23.53
+52,Male,50.3,1.78,170,132,67,0.85,555.0,HIIT,24.5,2.3,2,1,15.88
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+50,Male,86.7,1.63,161,143,58,1.7,1203.0,Cardio,12.1,3.5,4,3,32.63
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+29,Male,88.1,1.93,186,143,66,1.88,1479.0,Cardio,11.1,3.5,5,3,23.65
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+50,Female,69.7,1.72,198,135,66,1.24,753.0,Strength,27.3,2.0,4,2,23.56
+57,Female,60.9,1.52,196,157,66,1.31,926.0,Strength,30.6,1.8,3,2,26.36
+27,Male,76.5,1.97,180,165,61,1.13,1025.0,Strength,21.3,2.3,2,1,19.71
+46,Male,94.7,1.82,164,120,69,1.47,873.0,Cardio,23.2,3.2,3,2,28.59
+30,Male,67.9,1.75,180,168,67,1.25,1155.0,Yoga,29.1,3.1,2,1,22.17
+29,Male,121.1,1.87,188,130,52,1.31,937.0,HIIT,24.1,3.2,4,2,34.63
+48,Female,71.5,1.64,179,154,60,1.46,1012.0,Yoga,32.5,1.9,4,2,26.58
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+42,Male,88.4,1.93,188,158,65,1.68,1314.0,Yoga,12.4,3.5,4,3,23.73
+38,Male,88.4,1.79,182,138,68,1.74,1321.0,Yoga,12.5,3.5,5,3,27.59
+53,Female,53.3,1.74,173,156,54,1.37,962.0,Strength,32.2,1.8,3,2,17.6
+40,Female,58.1,1.56,174,136,63,0.61,415.0,Yoga,34.4,2.2,2,1,23.87
+33,Female,74.2,1.76,192,145,66,1.15,834.0,Strength,25.9,1.5,4,2,23.95
+56,Male,84.6,1.63,184,141,52,1.01,705.0,HIIT,20.2,2.9,4,2,31.84
+59,Male,78.0,1.92,180,127,53,0.72,453.0,Cardio,28.1,2.7,3,1,21.16
+56,Male,89.0,1.68,176,152,65,1.77,1332.0,Yoga,10.5,3.5,5,3,31.53
+31,Female,42.7,1.76,182,121,65,1.46,883.0,Strength,34.9,2.2,3,1,13.78
+48,Male,83.9,1.82,176,149,70,1.9,1401.0,HIIT,13.1,3.5,5,3,25.33
+22,Female,78.7,1.52,173,166,74,1.45,1204.0,Strength,31.5,2.3,3,2,34.06
+52,Female,65.9,1.52,188,139,53,1.27,794.0,Cardio,27.0,2.3,4,2,28.52
+40,Male,76.9,1.92,179,137,68,1.13,851.0,Yoga,22.4,3.7,4,2,20.86
+46,Male,84.3,2.0,189,162,66,1.83,1467.0,HIIT,13.3,3.5,5,3,21.07
+28,Female,62.4,1.6,178,129,62,1.1,710.0,Strength,34.1,1.7,3,2,24.37
+35,Male,63.7,1.74,198,143,71,1.03,810.0,HIIT,29.8,2.7,4,2,21.04
+29,Female,54.9,1.52,172,123,72,1.48,910.0,Strength,27.0,2.0,3,1,23.76
+26,Male,76.4,1.79,177,156,73,1.17,1004.0,Strength,21.3,2.7,3,2,23.84
+27,Male,106.4,1.86,182,123,50,1.34,907.0,Cardio,20.6,3.1,2,1,30.76
+34,Female,68.4,1.5,192,132,61,1.37,904.0,Cardio,33.7,2.1,4,2,30.4
+55,Male,63.5,1.86,198,144,53,1.36,969.0,Strength,22.9,2.9,4,2,18.35
+24,Female,59.4,1.5,199,132,60,1.25,825.0,Yoga,25.0,2.0,3,2,26.4
+30,Male,113.4,1.91,177,137,54,1.27,957.0,HIIT,22.2,3.7,3,1,31.08
+57,Female,61.9,1.52,168,143,51,0.92,592.0,HIIT,28.4,2.6,3,1,26.79
+59,Female,70.7,1.68,192,121,66,1.17,637.0,Cardio,34.9,1.9,3,2,25.05
+26,Male,111.5,1.9,191,167,57,1.02,937.0,Yoga,25.2,2.2,2,1,30.89
+44,Female,72.1,1.66,176,152,55,1.4,958.0,Strength,27.0,2.5,2,1,26.16
+19,Male,56.9,1.87,178,138,70,0.61,463.0,Cardio,23.3,3.7,3,1,16.27
+22,Male,86.2,1.9,186,128,58,1.91,1345.0,Strength,11.7,3.5,5,3,23.88
+46,Male,59.4,1.68,165,147,62,1.16,844.0,Strength,20.2,2.5,4,2,21.05
+54,Female,75.6,1.72,194,154,60,0.97,672.0,Cardio,26.4,1.9,3,1,25.55
+55,Female,76.3,1.73,196,158,65,1.08,768.0,Yoga,32.0,2.6,3,1,25.49
+36,Male,95.8,1.86,193,129,65,1.37,972.0,HIIT,21.2,3.2,3,1,27.69
+25,Male,120.6,1.83,179,120,51,1.04,686.0,Cardio,22.3,2.5,3,2,36.01
+18,Female,65.3,1.55,184,161,59,1.42,1143.0,Yoga,28.5,2.2,3,2,27.18
+39,Male,85.3,1.89,163,140,61,0.77,593.0,Strength,27.6,3.6,2,1,23.88
+34,Male,68.0,1.68,169,128,57,0.67,472.0,Cardio,25.8,3.4,2,1,24.09
+24,Female,52.6,1.57,169,166,60,1.29,1071.0,HIIT,31.9,2.4,2,1,21.34
+42,Male,63.3,1.77,183,134,59,1.32,876.0,HIIT,25.7,3.3,3,2,20.2
+21,Female,76.3,1.65,180,154,62,1.14,878.0,Strength,31.8,1.7,3,2,28.03
+53,Male,61.0,1.63,170,124,74,0.54,331.0,Cardio,20.0,3.4,3,1,22.96
+23,Female,67.8,1.61,186,160,66,1.34,1072.0,HIIT,34.3,1.8,2,1,26.16
+48,Male,114.9,1.86,175,141,56,1.36,949.0,Cardio,21.1,3.6,3,1,33.21
+36,Male,88.0,1.71,185,160,73,1.87,1646.0,HIIT,12.0,3.5,5,3,30.09
+56,Female,67.7,1.58,189,142,52,1.45,927.0,Strength,32.7,1.9,2,1,27.12
+44,Male,124.8,1.85,175,126,69,1.35,842.0,Strength,24.4,3.5,4,2,36.46
+27,Female,53.0,1.58,166,142,62,1.26,895.0,Cardio,34.5,1.8,4,2,21.23
+43,Male,61.2,1.88,175,163,68,1.03,831.0,Yoga,24.7,2.9,3,1,17.32
+36,Female,48.7,1.68,194,144,56,1.23,886.0,Yoga,33.7,1.6,3,1,17.25
+56,Female,50.6,1.7,175,146,58,1.29,848.0,Strength,33.1,2.3,2,1,17.51
+20,Female,72.6,1.73,199,160,68,1.27,1016.0,HIIT,34.8,2.5,3,2,24.26
+30,Female,70.4,1.56,171,125,62,1.03,644.0,Yoga,34.3,2.4,4,2,28.93
+45,Male,52.5,1.88,194,153,71,1.08,818.0,Strength,29.0,2.9,4,2,14.85
+37,Male,76.3,1.77,180,128,57,1.36,957.0,HIIT,27.3,2.1,4,2,24.35
+45,Female,41.2,1.58,195,168,71,1.47,1111.0,HIIT,28.7,2.6,2,1,16.5
+25,Female,55.3,1.76,176,152,67,1.56,1186.0,Yoga,15.5,2.7,4,3,17.85
+58,Female,49.7,1.67,191,125,72,1.38,776.0,Strength,31.9,2.3,4,2,17.82
+56,Male,48.3,1.87,165,156,51,0.68,525.0,HIIT,29.8,3.3,3,1,13.81
+18,Male,83.2,1.96,183,150,67,1.83,1510.0,HIIT,13.6,3.5,4,3,21.66
+20,Male,120.5,1.73,186,153,67,1.01,850.0,Cardio,29.3,3.4,3,2,40.26
+30,Male,121.6,2.0,179,160,60,1.24,1091.0,Cardio,28.0,3.3,4,2,30.4
+45,Female,73.0,1.75,184,148,50,1.14,759.0,Yoga,26.6,2.0,4,2,23.84
+42,Female,57.5,1.67,161,133,63,1.76,1053.0,Cardio,16.2,2.7,5,3,20.62
+50,Female,42.7,1.53,168,155,65,1.41,983.0,Strength,28.4,2.4,4,2,18.24
+55,Male,129.9,1.73,187,155,60,0.69,529.0,Yoga,21.7,3.1,3,1,43.4
+23,Male,108.6,1.92,197,134,50,0.89,656.0,Yoga,25.8,3.5,2,1,29.46
+49,Male,88.6,2.0,162,127,56,1.83,1150.0,Cardio,14.9,3.5,4,3,22.15
+38,Female,49.7,1.51,179,145,65,1.25,906.0,HIIT,32.6,2.5,3,1,21.8
+33,Female,56.4,1.54,165,155,63,1.06,822.0,Strength,29.2,1.5,3,1,23.78
+38,Male,46.9,1.74,198,167,64,1.03,946.0,Strength,20.0,2.6,4,2,15.49
+28,Female,71.3,1.67,192,140,63,1.46,1022.0,Strength,28.1,1.5,4,2,25.57
+54,Male,71.6,1.86,160,147,70,1.14,830.0,Yoga,25.3,2.2,3,2,20.7
+53,Male,82.3,1.61,196,123,60,1.87,1139.0,Strength,11.6,3.5,5,3,31.75
+52,Female,70.6,1.69,165,135,65,1.36,826.0,HIIT,26.1,2.2,3,1,24.72
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+37,Female,73.5,1.69,174,131,66,0.54,354.0,Cardio,27.5,1.6,2,1,25.73
+35,Male,85.6,1.85,185,135,55,1.78,1322.0,Strength,14.5,3.5,5,3,25.01
+58,Female,74.6,1.68,173,159,60,1.03,737.0,Yoga,26.0,2.0,4,2,26.43
+31,Female,63.2,1.8,166,157,66,0.59,463.0,Cardio,25.5,2.6,2,1,19.51
+32,Female,62.6,1.63,190,161,58,1.67,1344.0,HIIT,15.0,2.7,5,3,23.56
+48,Male,87.1,1.76,183,156,67,1.81,1398.0,Strength,11.6,3.5,5,3,28.12
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+20,Female,64.3,1.78,188,137,61,1.71,1171.0,Cardio,18.7,2.7,4,3,20.29
+33,Female,53.7,1.66,191,129,72,1.25,806.0,HIIT,29.5,2.5,3,1,19.49
+40,Male,63.0,2.0,176,161,71,1.42,1257.0,Strength,26.8,2.9,4,2,15.75
+28,Male,128.4,1.86,173,158,71,1.06,921.0,Strength,23.6,3.3,4,2,37.11
+29,Male,88.0,1.89,199,137,54,1.75,1319.0,Cardio,13.3,3.5,5,3,24.64
+27,Female,63.7,1.52,195,120,60,1.19,714.0,Strength,25.4,2.1,4,2,27.57
+49,Male,83.7,1.87,183,158,53,1.29,1009.0,HIIT,23.9,2.3,4,2,23.94
+33,Male,102.6,1.75,186,140,53,1.4,1078.0,HIIT,27.9,2.2,3,2,33.5
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+55,Male,87.5,1.88,191,134,73,1.19,789.0,Yoga,27.2,2.6,4,2,24.76
+29,Male,87.0,1.86,177,123,72,1.52,1028.0,Yoga,11.0,3.5,4,3,25.15
+41,Male,82.8,1.66,177,139,74,1.97,1355.0,HIIT,11.2,3.5,5,3,30.05
+45,Female,65.4,1.68,196,129,66,0.76,441.0,HIIT,35.0,2.6,3,1,23.17
+25,Female,47.2,1.71,185,142,56,0.89,632.0,Cardio,26.0,2.5,2,1,16.14
+45,Female,58.2,1.7,168,160,53,0.95,684.0,Yoga,26.8,2.0,3,1,20.14
+53,Female,73.5,1.55,194,153,72,1.17,806.0,HIIT,26.3,2.1,4,2,30.59
+43,Female,55.2,1.73,167,165,62,1.67,1240.0,Cardio,16.6,2.7,5,3,18.44
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+45,Female,73.5,1.74,181,152,50,1.18,807.0,Cardio,30.0,2.3,4,2,24.28
+45,Male,89.6,1.81,178,153,70,1.7,1287.0,Yoga,12.4,3.5,5,3,27.35
+54,Male,86.5,1.76,189,158,68,1.48,1158.0,Yoga,29.0,3.4,3,2,27.92
+58,Female,46.1,1.67,187,129,70,1.28,743.0,Yoga,25.3,1.8,4,2,16.53
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+34,Female,54.2,1.52,190,132,60,1.13,746.0,Cardio,31.0,2.2,3,1,23.46
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+37,Male,88.8,1.95,189,135,66,1.58,1173.0,Yoga,10.3,3.5,5,3,23.35
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+45,Female,57.2,1.55,194,121,58,1.67,909.0,Cardio,18.4,2.7,5,3,23.81
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+29,Female,71.8,1.55,190,120,68,1.2,720.0,Cardio,26.3,2.4,4,2,29.89
+18,Female,66.9,1.57,193,127,63,1.07,679.0,Cardio,30.7,2.2,3,2,27.14
+22,Female,48.7,1.76,165,167,66,1.37,1144.0,HIIT,29.0,2.2,4,2,15.72
+47,Male,66.2,1.71,181,169,71,1.42,1188.0,HIIT,22.8,2.9,3,1,22.64
+47,Female,40.0,1.76,173,132,63,0.8,475.0,Strength,26.7,2.2,3,1,12.91
+34,Male,112.4,1.85,162,134,66,1.18,870.0,HIIT,23.4,3.6,4,2,32.84
+40,Female,70.0,1.55,171,163,74,0.85,693.0,Yoga,31.1,1.5,3,1,29.14
+32,Male,84.0,1.74,195,127,63,1.45,1013.0,Strength,25.8,2.1,3,1,27.74
+54,Female,43.8,1.65,171,129,61,1.29,749.0,Yoga,26.6,1.7,4,2,16.09
+38,Male,123.3,1.62,161,165,65,1.34,1216.0,Yoga,21.2,2.3,4,2,46.98
+31,Female,57.9,1.56,161,124,50,1.8,1116.0,Yoga,15.1,2.7,5,3,23.79
+19,Male,82.4,1.96,174,169,62,1.9,1766.0,HIIT,10.1,3.5,5,3,21.45
+28,Male,84.8,1.79,182,138,55,1.35,1025.0,Cardio,28.8,2.3,4,2,26.47
+56,Male,109.6,1.66,188,166,66,1.29,1060.0,Cardio,20.5,3.1,3,2,39.77
+55,Male,86.4,1.77,176,157,61,1.0,777.0,Yoga,27.3,2.8,3,2,27.58
+51,Female,63.9,1.8,191,130,72,1.07,626.0,HIIT,33.7,2.7,4,2,19.72
+55,Female,70.8,1.62,166,162,68,0.54,394.0,Strength,26.0,1.8,3,1,26.98
+51,Male,115.3,1.67,189,139,55,1.38,950.0,Yoga,24.4,2.5,3,2,41.34
+35,Female,55.3,1.56,187,139,56,1.71,1188.0,Strength,19.8,2.7,5,3,22.72
+47,Male,73.9,1.8,173,140,50,1.36,942.0,Strength,28.3,3.7,3,1,22.81
+32,Male,97.3,1.96,189,137,50,1.36,1025.0,HIIT,24.9,3.4,3,2,25.33
+44,Female,60.4,1.65,176,169,54,1.27,966.0,HIIT,32.6,2.1,4,2,22.19
+51,Male,49.3,1.61,191,129,55,1.36,868.0,Cardio,28.3,2.9,2,1,19.02
+55,Male,91.8,1.78,165,153,65,1.44,1091.0,Cardio,23.5,3.4,3,1,28.97
+50,Female,73.6,1.55,183,145,74,0.89,581.0,HIIT,33.9,2.1,3,1,30.63
+41,Male,47.1,1.78,165,149,65,1.26,929.0,Yoga,24.7,2.4,3,1,14.87
+32,Male,65.2,1.62,186,128,63,0.58,408.0,Cardio,25.3,3.4,2,1,24.84
+47,Male,87.2,1.64,177,165,50,2.0,1634.0,Strength,15.0,3.5,4,3,32.42
+59,Male,96.7,1.71,183,150,62,0.57,423.0,Yoga,28.4,2.9,3,1,33.07
+34,Female,46.9,1.61,196,161,69,1.02,821.0,Strength,28.6,1.7,3,2,18.09
+22,Female,72.1,1.62,183,123,59,1.38,849.0,HIIT,25.1,2.1,3,1,27.47
+46,Female,62.5,1.54,197,125,73,1.61,906.0,Strength,18.1,2.7,4,3,26.35
+21,Male,110.7,1.69,193,133,59,1.06,775.0,Yoga,27.0,2.7,3,1,38.76
+27,Male,102.6,1.83,199,123,58,1.41,954.0,HIIT,26.5,3.5,3,2,30.64
+34,Female,64.3,1.62,174,145,62,1.76,1276.0,Yoga,19.8,2.7,5,3,24.5
+27,Male,80.7,1.61,170,166,52,1.75,1598.0,Strength,14.3,3.5,4,3,31.13
+34,Male,115.3,1.81,181,167,67,1.36,1249.0,Yoga,24.5,2.2,2,1,35.19
+37,Male,87.8,1.69,183,158,69,1.81,1573.0,HIIT,10.3,3.5,5,3,30.74
+41,Male,74.3,1.61,160,136,51,1.06,714.0,Strength,24.8,3.5,4,2,28.66
+22,Female,78.5,1.7,177,138,66,1.36,938.0,Strength,31.9,2.1,4,2,27.16
+51,Female,77.3,1.58,171,168,68,0.97,733.0,Cardio,31.1,2.0,2,1,30.96
+23,Female,64.5,1.7,187,125,72,0.51,319.0,Cardio,30.4,1.7,2,1,22.32
+19,Male,72.0,1.94,199,148,62,1.17,952.0,Yoga,24.7,3.2,3,2,19.13
+30,Female,77.9,1.77,166,130,62,1.41,916.0,Cardio,27.9,2.6,4,2,24.87
+28,Female,69.6,1.58,182,135,50,1.49,1006.0,Cardio,27.8,2.0,3,2,27.88
+40,Male,68.9,1.99,180,149,51,1.16,951.0,Cardio,25.0,2.4,4,2,17.4
+33,Male,45.9,1.89,193,144,50,1.45,1148.0,Cardio,21.6,2.9,3,2,12.85
+48,Female,68.2,1.69,170,159,73,0.75,537.0,HIIT,28.4,2.5,3,1,23.88
+28,Female,79.6,1.59,181,130,54,1.49,968.0,HIIT,28.2,1.5,3,2,31.49
+33,Female,53.6,1.52,194,126,73,1.02,643.0,Cardio,30.9,2.3,2,1,23.2
+25,Female,57.7,1.58,188,145,52,1.36,986.0,Yoga,25.7,2.2,3,2,23.11
+21,Male,74.2,1.93,187,141,56,1.32,1024.0,HIIT,28.3,2.5,3,2,19.92
+57,Female,64.4,1.56,177,144,73,1.52,985.0,Cardio,17.8,2.7,5,3,26.46
+21,Female,54.9,1.78,173,138,71,0.55,380.0,HIIT,31.7,2.0,2,1,17.33
+42,Male,50.3,1.64,177,150,62,0.63,468.0,Strength,27.0,2.4,3,1,18.7
+20,Female,46.5,1.68,187,145,72,0.91,660.0,HIIT,27.9,2.4,2,1,16.48
+49,Male,57.8,1.99,183,161,67,0.93,741.0,HIIT,24.7,3.1,3,1,14.6
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+44,Male,82.5,1.79,167,155,70,1.51,1159.0,HIIT,13.8,3.5,5,3,25.75
+46,Female,66.5,1.73,163,142,56,1.11,709.0,Yoga,34.7,2.0,3,1,22.22
+49,Female,70.2,1.67,186,166,50,0.93,695.0,Strength,32.5,1.8,3,1,25.17
+36,Male,121.9,1.93,195,132,54,1.49,1082.0,Cardio,21.8,3.4,3,2,32.73
+38,Female,70.3,1.52,164,144,66,1.19,857.0,Yoga,25.6,2.0,4,2,30.43
+22,Female,40.3,1.56,192,121,57,1.45,877.0,HIIT,30.8,2.1,4,2,16.56
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+45,Male,79.2,1.95,189,135,69,1.47,982.0,Yoga,22.8,2.8,3,2,20.83
+59,Female,75.8,1.77,167,128,53,1.46,841.0,Strength,33.1,1.8,3,1,24.19
+39,Male,71.7,1.88,198,147,51,0.55,445.0,Cardio,23.7,2.2,2,1,20.29
+38,Female,57.7,1.56,185,132,54,1.64,1082.0,HIIT,16.6,2.7,5,3,23.71
+23,Female,47.4,1.6,196,154,50,0.76,585.0,Strength,33.5,1.6,3,1,18.52
+18,Female,57.2,1.75,193,160,74,1.29,1032.0,Cardio,33.5,2.4,3,2,18.68
+22,Female,44.8,1.72,166,132,54,1.48,977.0,Cardio,28.6,1.8,4,2,15.14
+58,Male,90.2,1.72,199,150,55,0.9,668.0,HIIT,28.3,2.1,2,1,30.49
+29,Male,84.9,1.72,187,140,51,1.79,1378.0,Yoga,14.3,3.5,4,3,28.7
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+31,Female,60.5,1.77,187,127,56,1.72,1092.0,HIIT,15.0,2.7,4,3,19.31
+43,Female,53.8,1.72,173,160,71,1.03,742.0,Strength,30.2,2.3,4,2,18.19
+44,Male,86.6,1.97,197,167,50,1.66,1372.0,Yoga,11.7,3.5,5,3,22.31
+26,Female,72.8,1.67,163,121,50,1.37,829.0,Strength,35.0,1.9,2,1,26.1
+43,Female,66.3,1.77,177,157,57,1.48,1046.0,HIIT,32.7,1.7,4,2,21.16
+39,Female,56.0,1.6,184,151,69,0.89,672.0,Yoga,32.6,2.0,2,1,21.87
+47,Male,45.9,1.93,166,159,65,1.29,1015.0,Cardio,29.6,2.0,3,2,12.32
+34,Male,88.0,1.64,197,147,70,1.9,1536.0,Yoga,14.4,3.5,4,3,32.72
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+53,Female,44.7,1.79,164,160,72,1.34,965.0,Yoga,26.7,2.1,3,2,13.95
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+25,Male,95.6,1.9,190,160,72,1.47,1294.0,Cardio,23.8,2.4,4,2,26.48
+52,Male,47.8,1.86,179,129,67,1.01,645.0,Yoga,24.7,2.8,3,2,13.82
+32,Male,85.3,1.64,178,123,69,1.77,1197.0,Cardio,13.2,3.5,4,3,31.71
+39,Male,59.7,1.83,170,154,59,1.35,1143.0,Strength,29.1,2.3,4,2,17.83
+31,Male,70.2,1.98,199,129,61,0.71,504.0,Yoga,24.2,2.5,2,1,17.91
+43,Female,72.7,1.8,162,166,50,0.79,590.0,Cardio,32.8,2.1,3,1,22.44
+45,Female,50.4,1.79,182,130,59,0.74,433.0,Cardio,27.7,2.5,2,1,15.73
+40,Male,81.7,1.74,166,133,58,0.91,666.0,Yoga,27.0,2.7,2,1,26.99
+31,Male,49.3,1.62,182,157,52,0.56,484.0,Strength,20.0,3.2,2,1,18.79
+41,Female,61.9,1.64,166,131,68,1.93,1138.0,Yoga,19.4,2.7,4,3,23.01
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+43,Male,66.5,1.83,178,136,59,1.11,747.0,Yoga,27.1,2.4,3,2,19.86
+31,Male,75.5,1.93,168,135,68,0.6,446.0,Cardio,20.5,3.4,2,1,20.27
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+40,Female,72.2,1.6,173,130,67,0.87,566.0,Yoga,29.2,1.9,3,1,28.2
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+55,Male,84.7,1.79,178,122,52,1.19,719.0,Cardio,23.3,2.4,3,2,26.43
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+32,Male,77.4,1.92,174,137,50,1.39,1047.0,Cardio,26.7,3.3,3,2,21.0
+42,Female,72.9,1.62,175,139,67,1.17,732.0,Yoga,25.7,2.0,4,2,27.78
+54,Female,58.4,1.59,186,166,73,1.08,807.0,Cardio,32.5,1.9,2,1,23.1
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+56,Male,58.7,1.86,187,159,65,1.09,858.0,Cardio,22.6,3.3,3,2,16.97
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+42,Female,56.7,1.53,192,149,74,1.07,717.0,Strength,27.4,1.9,3,1,24.22
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+28,Female,55.1,1.79,192,159,67,1.99,1582.0,Yoga,17.0,2.7,5,3,17.2
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+41,Female,66.8,1.74,164,149,57,1.34,898.0,Yoga,29.2,2.3,3,1,22.06
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