Add initial project dependencies and model artifacts

app.py

@@ -0,0 +1,949 @@
+import os
+import numpy as np
+import pandas as pd
+import joblib
+import pickle
+import streamlit as st
+import seaborn as sns
+from streamlit_option_menu import option_menu
+import time
+import matplotlib.pyplot as plt
+import json
+import google.generativeai as genai
+from dotenv import load_dotenv
+from transformers import pipeline
+
+# Set page config with icon
+st.set_page_config(page_title="Disease Prediction", page_icon="🩺", layout="wide")
+
+diabetes_model = pickle.load(open('diabetes/diabetes_model.sav', 'rb'))
+# asthama_model = pickle.load(open('asthama/model.pkl', 'rb'))
+
+import joblib
+asthama_model = joblib.load("asthama/model.pkl")
+
+cardio_model = pickle.load(open('cardio_vascular/xgboost_cardiovascular_model.pkl', 'rb'))
+
+# stroke_model = pickle.load(open('stroke/stroke_model.sav', 'rb'))
+
+stroke_model = joblib.load("stroke/finalized_model.pkl")
+
+prep_asthama = pickle.load(open('asthama/preprocessor.pkl', 'rb'))
+
+# sleep_model = pickle.load(open('sleep_health/best_model.pkl', 'rb'))
+# scaler = pickle.load(open('sleep_health/scaler.pkl', 'rb'))
+# label_encoder = pickle.load(open('sleep_health/label_encoders.pkl', 'rb'))
+
+# At the beginning of your app, when loading models:
+try:
+    sleep_model = pickle.load(open('sleep_health/svc_model.pkl', 'rb'))
+    scaler = pickle.load(open('sleep_health/scaler.pkl', 'rb'))
+    label_encoder = pickle.load(open('sleep_health/label_encoders.pkl', 'rb'))
+    
+    # Store the expected feature names if available
+    # This depends on how your model was saved/trained
+    # if hasattr(sleep_model, 'feature_names_in_'):
+    #     expected_features = sleep_model.feature_names_in_
+    # else:
+    #     # Try to load feature names from a separate file
+    #     try:
+    #         with open('sleep_health/feature_names.pkl', 'rb') as f:
+    #             expected_features = pickle.load(f)
+    #     except:
+    #         st.warning("Warning: Feature names not found. Predictions may be inaccurate.")
+    #         expected_features = None
+            
+except FileNotFoundError:
+    st.error("Error: Model files not found. Please upload the model files.")
+    st.stop()
+
+with st.sidebar:
+    st.title("🩺 Disease Prediction")
+    
+    selected = option_menu(
+        menu_title="Navigation",
+        options=['Home',  'Diabetes Prediction','Hypertension Prediction', 'Cardiovascular Disease Prediction', 'Stroke Prediction','Asthma Prediction', 'Sleep Health Analysis','Mental-Analysis','Medical Consultant', 'Data Visualization'],
+        icons=['house', 'activity', 'lungs', 'heart-pulse', 'brain', 'bar-chart', 'chat'],
+        menu_icon="cast",
+        default_index=0,
+        styles={
+            "container": {"padding": "5px", "background-color": "#111111"},  # Darker background
+            "icon": {"color": "#FF0000", "font-size": "20px"},  # Red icons
+            "nav-link": {"font-size": "16px", "text-align": "left", "margin": "0px", "color": "#FFFFFF"},  # White text
+            "nav-link-selected": {"background-color": "#FF0000", "color": "#FFFFFF"},
+        }
+    )
+
+
+# 'Checkbox-to-disease-predictor', 
+# 'Text-based Disease Prediction', 
+# Utility function to safely convert input to float
+def safe_float(value, default=0.0):
+    try:
+        return float(value)
+    except ValueError:
+        return default  # Assigns default value if conversion fails
+
+
+# 🚀 Home Page
+if selected == 'Home':
+    st.title("🩺 Early Prediction of Health & Lifestyle Diseases")
+
+    st.markdown("""
+    ## Welcome to the **Early Prediction of Health & Lifestyle Diseases**!  
+    This tool provides **early prediction and analysis** for various health conditions using **Machine Learning & NLP**.
+    
+    ### 🏥 Available Features:
+    - **✅ Checkbox-based Lifestyle Disease Predictor** using **BiomedNLP-PubMedBERT**  
+    - **🤖 AI Chatbot for Health Assistance** (Ask health-related questions)  
+    - **🧠 Mental Health Assessment**  
+    - **🩸 Disease Predictors**:
+      - Diabetes  
+      - Asthma  
+      - Stroke  
+      - Cardiovascular Disease  
+    - **📊 Data Visualizer** (Analyze trends in health conditions)  
+                
+    👉 Select an option from the sidebar to proceed!  
+    """)
+
+    with st.expander("🚀 Quick Start Guide"):
+        st.write("""
+        1. Select a **health prediction model** from the sidebar.
+        2. Enter your details in the input fields.
+        3. Click **Predict** to get your result.
+        4. View personalized **health insights & recommendations**.
+        """)
+
+
+    # Disclaimer Section
+    st.markdown("---")
+    # st.markdown("""
+    # **⚠️ Disclaimer:** This application has been developed using **real-world healthcare datasets** sourced from Kaggle:  
+
+    # - [Stroke Prediction Dataset](http://kaggle.com/code/chanchal24/stroke-prediction-using-python/input?select=healthcare-dataset-stroke-data.csv)  
+    # - [Asthma Analysis & Prediction](https://www.kaggle.com/code/bryamblasrimac/asthma-eda-prediction-f2score-85/input)  
+    # - [Diabetes Dataset](https://www.kaggle.com/datasets/mathchi/diabetes-data-set)  
+    # - [Cardiovascular Disease Dataset](https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset)  
+    # - [Sentiment Analysis for Mental Health](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health)  
+    # - [Sleep Health Analysis](https://www.kaggle.com/datasets/uom190346a/sleep-health-and-lifestyle-dataset)
+    # \
+
+
+    st.markdown("""
+    The predictions are generated using **machine learning models** trained on these datasets, incorporating **evaluation metrics and graphical insights** to enhance interpretability.  
+
+    However, this tool has **not undergone clinical validation** and should be used **for informational and educational purposes only**. It is not intended to serve as a substitute for professional medical diagnosis or treatment. Always consult a qualified healthcare provider for medical advice.
+    """)
+
+
+if selected == 'Diabetes Prediction':
+    st.title('🩸 Diabetes Prediction using ML (SVC)')
+    st.image("https://cdn-icons-png.flaticon.com/512/2919/2919950.png", width=100)
+
+    st.markdown("""
+    This model predicts the likelihood of **Diabetes** based on various health parameters.  
+    Please enter the required medical details below and click **"Diabetes Test Result"** to get the prediction.
+    """)
+
+
+
+    # Create columns for better input organization
+    col1, col2 = st.columns(2)
+
+    with col1:
+        Pregnancies = safe_float(st.text_input("Number of Pregnancies", "0"))
+        Glucose = safe_float(st.text_input("Glucose Level", "100"))
+        BloodPressure = safe_float(st.text_input("Blood Pressure", "80"))
+        SkinThickness = safe_float(st.text_input("Skin Thickness", "20"))
+
+    with col2:
+        Insulin = safe_float(st.text_input("Insulin Level", "79"))
+        BMI = safe_float(st.text_input("BMI (Body Mass Index)", "25.0"))
+        DiabetesPedigreeFunction = safe_float(st.text_input("Diabetes Pedigree Function", "0.5"))
+        Age = st.number_input("Enter Age", min_value=10, max_value=100, value=30, step=1)
+
+    with col1:
+        if st.button('Diabetes Test Result'):
+            try:
+                input_data = np.array([[Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]])
+                
+                with st.spinner("⏳ Predicting... Please wait..."):
+                    time.sleep(2)  # Simulating delay (remove in actual use)
+                    diab_prediction = diabetes_model.predict(input_data)
+
+                
+                
+                result = "🛑 The person is diabetic" if diab_prediction[0] == 1 else "✅ The person is not diabetic"
+                if diab_prediction[0] == 0:
+                    # st.balloons()  # Or use st.confetti() if you install the library
+                    st.success(result)
+
+            except Exception as e:
+                st.error(f"❌ Error: {e}")
+
+
+if selected == 'Asthma Prediction':
+    st.title('🌬️ Asthma Prediction using ML')
+    st.image("https://cdn-icons-png.flaticon.com/512/3462/3462191.png", width=100)
+
+    st.markdown("""
+    This model predicts the likelihood of **Asthma** based on health factors.  
+    Enter your details and click **"Asthma Test Result"** to get the prediction.
+    """)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        Gender_Male = st.radio("Gender", ["Female", "Male"])
+        Gender_Male = 1 if Gender_Male == "Male" else 0
+
+        Smoking_Status = st.radio("Smoking Status", ["Non-Smoker", "Ex-Smoker"])
+        Smoking_Status_Ex_Smoker = 1 if Smoking_Status == "Ex-Smoker" else 0
+        Smoking_Status_Non_Smoker = 1 if Smoking_Status == "Non-Smoker" else 0
+
+    with col2:
+        # Use actual age as input instead of normalized value
+        actual_age = st.slider("Age", min_value=18, max_value=85, value=40, help="Your actual age in years")
+        
+        # Convert actual age to normalized value (0.0 to 0.914894)
+        # Assuming normalization was done with min_age=18 and max_age=90
+        min_age = 18
+        max_age = 90
+        Age = (actual_age - min_age) / (max_age - min_age)
+        
+        # Show the normalized value for reference (can be hidden in final version)
+        st.info(f"Normalized age value (used by model): {Age:.6f}")
+        
+        Peak_Flow = st.slider("Peak Flow (L/sec)", min_value=0.1, max_value=1.0, value=0.5)
+
+    with col1:
+        if st.button('Asthma Test Result'):
+            try:
+                # Prepare raw input
+                raw_input = np.array([[Gender_Male, Smoking_Status_Ex_Smoker, Smoking_Status_Non_Smoker, Age, Peak_Flow]])
+
+                # Check if preprocessing is needed
+                if prep_asthama is not None and hasattr(prep_asthama, "transform"):
+                    processed_input = prep_asthama.transform(raw_input)  # Use transform if prep_asthama exists
+                else:
+                    processed_input = raw_input  # If no scaler, use raw input
+
+                with st.spinner("⏳ Predicting... Please wait..."):
+                    time.sleep(2)  # Simulating delay (remove in actual use)
+                    asthma_prediction = asthama_model.predict(processed_input)
+
+                result = "🛑 High risk of asthma" if asthma_prediction[0] == 1 else "✅ Low risk of asthma"
+                if asthma_prediction[0] == 0:
+                    # st.balloons()
+                    st.success(result)
+                else:
+                    st.error(result)
+                
+                # Add risk factor analysis
+                st.subheader("Risk Factor Analysis")
+                risk_factors = []
+                
+                if actual_age > 60:
+                    risk_factors.append("⚠️ Age is a risk factor for asthma")
+                if Smoking_Status == "Ex-Smoker":
+                    risk_factors.append("⚠️ Smoking history increases asthma risk")
+                if Peak_Flow < 0.5:
+                    risk_factors.append("⚠️ Low peak flow readings may indicate restricted airways")
+                
+                if risk_factors:
+                    for factor in risk_factors:
+                        st.markdown(factor)
+                else:
+                    st.markdown("✅ No significant risk factors identified")
+
+            except Exception as e:
+                st.error(f"❌ Error: {e}")
+                st.info("If you have access to the preprocessing pipeline, you can check the exact age normalization formula used during model training.")
+
+
+if selected == 'Cardiovascular Disease Prediction':
+    st.title('❤️ Cardiovascular Disease Prediction')
+    st.image("https://cdn-icons-png.flaticon.com/512/2919/2919950.png", width=100)
+
+    st.markdown("""
+    This model predicts the likelihood of **Cardiovascular Disease** based on various health parameters.  
+    Please enter the required medical details below and click **"Cardio Test Result"** to get the prediction.
+    """)
+
+    # Input Fields
+    col1, col2 = st.columns(2)
+
+    with col1:
+        age = st.number_input("Age", min_value=29, max_value=64, value=40, step=1)
+        ap_hi = st.slider("Systolic Blood Pressure (ap_hi)", min_value=90, max_value=180, value=120)
+        ap_lo = st.slider("Diastolic Blood Pressure (ap_lo)", min_value=60, max_value=120, value=80)
+        weight = st.number_input("Weight (kg)", min_value=40.0, max_value=180.0, value=70.0, step=0.1)
+
+    with col2:
+        cholesterol = st.radio("Cholesterol Level", ["Normal", "Above Normal", "Well Above Normal"])
+        cholesterol = {"Normal": 1, "Above Normal": 2, "Well Above Normal": 3}[cholesterol]
+
+        gluc = st.radio("Glucose Level", ["Normal", "Above Normal", "Well Above Normal"])
+        gluc = {"Normal": 1, "Above Normal": 2, "Well Above Normal": 3}[gluc]
+
+        smoke = st.radio("Smoking Status", ["No", "Yes"])
+        smoke = 1 if smoke == "Yes" else 0
+
+        alco = st.radio("Alcohol Consumption", ["No", "Yes"])
+        alco = 1 if alco == "Yes" else 0
+
+        active = st.radio("Physically Active", ["No", "Yes"])
+        active = 1 if active == "Yes" else 0
+
+    # Prediction Button
+    if st.button('Cardio Test Result'):
+        try:
+            # Preparing Input Data
+            input_data = np.array([[age, ap_hi, ap_lo, cholesterol, gluc, smoke, alco, active, weight]])
+
+            with st.spinner("⏳ Predicting... Please wait..."):
+                time.sleep(2)  # Simulating Model Inference
+                cardio_prediction = cardio_model.predict(input_data)
+
+            # Display Result
+            result = "🛑 High risk of cardiovascular disease" if cardio_prediction[0] == 1 else "✅ Low risk of cardiovascular disease"
+            if cardio_prediction[0] == 0:
+                # st.balloons()
+                st.success(result)
+
+        except Exception as e:
+            st.error(f"❌ Error: {e}")
+
+
+
+
+
+if selected == 'Stroke Prediction':
+    st.title('🧠 Stroke Prediction using ML')
+    st.image("https://cdn-icons-png.flaticon.com/512/3209/3209265.png", width=100)
+
+    st.markdown("""
+    This model predicts the likelihood of **Stroke** based on various health factors.  
+    Enter your details and click **"Stroke Test Result"** to get the prediction.
+    """)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        Age = st.number_input("Age", min_value=0, max_value=82, value=50, step=1)
+        Hypertension = st.radio("Hypertension", ["No", "Yes"])
+        Hypertension = 1 if Hypertension == "Yes" else 0
+
+        Heart_Disease = st.radio("Heart Disease", ["No", "Yes"])
+        Heart_Disease = 1 if Heart_Disease == "Yes" else 0
+
+    with col2:
+        Ever_Married = st.radio("Ever Married", ["No", "Yes"])
+        Ever_Married = 1 if Ever_Married == "Yes" else 0
+
+        Avg_Glucose_Level = st.slider("Average Glucose Level", min_value=55.23, max_value=267.61, value=120.0)
+        BMI = st.slider("BMI", min_value=13.5, max_value=97.6, value=25.0)
+
+        Smoking_Status = st.selectbox("Smoking Status", ["Never Smoked", "Former Smoker", "Smokes", "Unknown"])
+        Smoking_Status = {"Never Smoked": 0, "Former Smoker": 1, "Smokes": 2, "Unknown": 3}[Smoking_Status]
+
+    with col1:
+        if st.button('Stroke Test Result'):
+            try:
+                input_data = np.array([[Age, Hypertension, Heart_Disease, Ever_Married, Avg_Glucose_Level, BMI, Smoking_Status]])
+
+                with st.spinner("⏳ Predicting... Please wait..."):
+                    time.sleep(2)
+                    stroke_prediction = stroke_model.predict(input_data)
+
+                result = "🛑 High risk of stroke" if stroke_prediction[0] == 1 else "✅ Low risk of stroke"
+                if stroke_prediction[0] == 0:
+                    st.balloons()
+                st.success(result)
+
+            except Exception as e:
+                st.error(f"❌ Error: {e}")
+
+
+
+
+if selected == 'Data Visualization':
+    # st.set_page_config(page_title="Data Visualizer",
+    #                 page_icon="📊", layout="centered")
+    st.title(" 📊 Data Visualization")
+
+    working_dir = os.path.dirname(os.path.abspath(__file__))
+
+    folder_path = f"{working_dir}/data_csv"
+
+    files_list = [f for f in os.listdir(folder_path) if f.endswith('.csv')]
+
+    selected_file = st.selectbox("Select a file", files_list, index=None)
+
+    if selected_file:
+
+        file_path = os.path.join(folder_path, selected_file)
+
+        df = pd.read_csv(file_path)
+
+        columns = df.columns.tolist()
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            st.write("")
+            st.write(df.head())
+
+        with col2:
+            x_axis = st.selectbox("Select X-axis", options=columns + ["None"])
+            y_axis = st.selectbox("Select Y-axis", options=columns + ["None"])
+
+            plot_list = ["Line Plot", "Bar Plot", "Scatter Plot", "Histogram", "Box Plot", "Distribution Plot", "Count Plot", "Pair Plot"]
+
+            selected_plot = st.selectbox("Select a plot", options=plot_list, index=None)
+
+            # st.write(x_axis)
+            # st.write(y_axis)
+            # st.write(selected_plot)
+
+        if st.button("Generate Plot"):
+
+            fig, ax = plt.subplots(figsize=(6,4))
+
+            if selected_plot == "Line Plot":
+                sns.lineplot(x=x_axis, y=y_axis, data=df, ax=ax)
+
+            elif selected_plot == "Bar Plot":
+                sns.barplot(x=x_axis, y=y_axis, data=df, ax=ax)
+            
+            elif selected_plot == "Scatter Plot":
+                sns.scatterplot(x=x_axis, y=y_axis, data=df, ax=ax)
+            
+            elif selected_plot == "Histogram":
+                sns.histplot(df[x_axis], ax=ax)
+            
+            elif selected_plot == "Box Plot":
+                sns.boxplot(x=x_axis, y=y_axis, data=df, ax=ax)
+
+            elif selected_plot == "Distribution Plot":
+                sns.kdeplot(df[x_axis], ax=ax)
+            
+            elif selected_plot == "Count Plot":
+                sns.countplot(x=x_axis, data=df, ax=ax)
+            
+            elif selected_plot == "Pair Plot":
+                sns.pairplot(df, ax=ax)
+
+            ax.tick_params(axis="x", labelsize=10)
+            ax.tick_params(axis="y", labelsize=10)
+
+            plt.title(f"{selected_plot} of {x_axis} vs {y_axis}", fontsize=12)
+            plt.xlabel(x_axis, fontsize=10)
+            plt.ylabel(y_axis, fontsize=10)
+            
+            st.pyplot(fig)
+
+
+if selected == 'Medical Consultant':
+    st.title("🩺 Medical Consultant Chatbot")
+    st.markdown("### Discuss Your Health Concerns with Our AI-powered Chatbot")
+    st.write("Ask about **Diabetes, Asthma, Stroke, Cardiovascular Disease, or Mental Health.**")
+
+    genai.configure(api_key="AIzaSyAwyi9c5OdvLoWrv5lFi1jZDEYwuprQAKE")
+
+    # Custom Styling
+    st.markdown("""
+        <style>
+            .prompt-box { 
+                background-color: #000000; 
+                padding: 12px; 
+                border-radius: 8px; 
+                font-size: 14px; 
+                font-family: sans-serif;
+                margin-bottom: 10px;
+                border: 1px solid #dee2e6;
+                text-align: center;
+            }
+        </style>
+    """, unsafe_allow_html=True)
+
+    st.markdown("#### 💡 Common Health Queries")
+
+    prompt_options = [
+        ("Diabetes – Diet", "What foods should I eat if I have diabetes?"),
+        ("Diabetes – Exercise", "What type of workouts help control blood sugar levels?"),
+        ("Asthma – Triggers", "What are common asthma triggers?"),
+        ("Asthma – Treatment", "What are the best medications for asthma?"),
+        ("Stroke – Symptoms", "What are the early warning signs of a stroke?"),
+        ("Stroke – Prevention", "How can I reduce my risk of stroke?"),
+        ("Cardiovascular – Heart Health", "How can I reduce my risk of heart disease?"),
+        ("Cardiovascular – Blood Pressure", "What lifestyle changes can lower high blood pressure?"),
+        ("Mental Health – Stress Management", "How can I manage stress effectively?"),
+        ("Mental Health – Sleep Disorders", "What are the causes and treatments for sleep disorders?")
+    ]
+
+    # Display prompts in two columns (2 prompts per row)
+    cols = st.columns(2)
+    for i in range(0, len(prompt_options), 2):
+        with cols[0]: 
+            if i < len(prompt_options):
+                label, prompt = prompt_options[i]
+                st.markdown(f"""<div class="prompt-box"><strong>{label}</strong><br>{prompt}</div>""", unsafe_allow_html=True)
+
+        with cols[1]: 
+            if i+1 < len(prompt_options):
+                label, prompt = prompt_options[i+1]
+                st.markdown(f"""<div class="prompt-box"><strong>{label}</strong><br>{prompt}</div>""", unsafe_allow_html=True)
+
+    # Initialize chat history if not present
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = []
+
+    # Display previous chat history
+    for message in st.session_state.chat_history:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # User input field
+    user_prompt = st.chat_input("Ask about Diabetes, Asthma, Stroke, Cardiovascular Disease, or Mental Health...")
+
+    # List of allowed topics
+    allowed_keywords = ["diabetes", "asthma", "stroke", "cardiovascular", "heart", "blood pressure", 
+                        "mental health", "depression", "stress", "cholesterol", "sleep disorders"]
+
+    if user_prompt:
+        # Display user message
+        st.chat_message("user").markdown(user_prompt)
+        st.session_state.chat_history.append({"role": "user", "content": user_prompt})
+
+        # Restriction: Only process if related to health topics
+        if any(keyword in user_prompt.lower() for keyword in allowed_keywords):
+            model = genai.GenerativeModel("gemini-2.0-flash")
+            response = model.generate_content(user_prompt)
+
+            if response and hasattr(response, "text"):
+                assistant_response = response.text
+            else:
+                assistant_response = "I'm sorry, I couldn't generate a response."
+
+            st.session_state.chat_history.append({"role": "assistant", "content": assistant_response})
+
+            # Display assistant's response
+            with st.chat_message("assistant"):
+                st.markdown(assistant_response)
+        else:
+            # Restriction message
+            restriction_msg = "**⚠️ This chatbot only responds to health-related topics.**\nPlease ask about Diabetes, Asthma, Stroke, Cardiovascular Disease, or Mental Health."
+            st.session_state.chat_history.append({"role": "assistant", "content": restriction_msg})
+            
+            with st.chat_message("assistant"):
+                st.markdown(restriction_msg)
+
+
+# if selected == 'Checkbox-to-disease-predictor':
+# # Load transformer model
+#     classifier = pipeline("zero-shot-classification", model="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
+#     # Use a pipeline as a high-level helper
+
+#     # from transformers import pipeline
+
+#     # pipe = pipeline("fill-mask", model="microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext")
+
+
+#     # Define symptoms for each disease
+#     diseases = {
+#         "Diabetes": ["Frequent urination", "Increased thirst", "Unexplained weight loss", "Fatigue", "Blurred vision"],
+#         "Hypertension": ["Headache", "Dizziness", "Chest pain", "Shortness of breath", "Nosebleeds"],
+#         "Obesity": ["Excess body fat", "Breathlessness", "Joint pain", "Increased sweating", "Low energy levels"],
+#         "Cardiovascular Disease": ["Chest pain", "Shortness of breath", "Dizziness", "Irregular heartbeat", "Fatigue"],
+#         "COPD": ["Chronic cough", "Shortness of breath", "Wheezing", "Chest tightness", "Frequent respiratory infections"],
+#         "Liver Disease": ["Jaundice", "Abdominal pain", "Swelling in legs", "Chronic fatigue", "Nausea"],
+#         "Kidney Disease": ["Swelling in legs", "Fatigue", "Loss of appetite", "Changes in urination", "Muscle cramps"],
+#         "Metabolic Syndrome": ["High blood sugar", "High blood pressure", "Increased waist size", "High cholesterol", "Fatigue"],
+#         "Osteoarthritis": ["Joint pain", "Stiffness", "Swelling", "Reduced flexibility", "Bone spurs"],
+#         "Gastroesophageal Reflux Disease": ["Heartburn", "Acid reflux", "Difficulty swallowing", "Chronic cough", "Sore throat"],
+#         "Depression": ["Persistent sadness", "Loss of interest", "Sleep disturbances", "Fatigue", "Difficulty concentrating"],
+#         "Sleep Apnea": ["Loud snoring", "Pauses in breathing", "Daytime drowsiness", "Morning headaches", "Irritability"],
+#     }
+
+#     # Streamlit UI
+#     st.title("🩺 Hybrid Symptom Checker")
+#     st.write("Select your symptoms and get AI-powered predictions!")
+
+#     selected_symptoms = []
+
+#     # Create symptom selection with markdown separation and three columns
+#     disease_keys = list(diseases.keys())
+
+#     for i in range(0, len(disease_keys), 3):
+#         cols = st.columns(3)
+#         for j in range(3):
+#             if i + j < len(disease_keys):
+#                 disease = disease_keys[i + j]
+#                 with cols[j]:
+#                     st.markdown(f"### {disease}")
+#                     for symptom in diseases[disease]:
+#                         if st.checkbox(symptom, key=f"{disease}_{symptom}"):
+#                             selected_symptoms.append(symptom)
+
+#     if st.button("🔍 Predict Disease"):
+#         if selected_symptoms:
+#             user_input = ", ".join(selected_symptoms)  # Convert symptoms to text
+
+#             # 1️⃣ Custom Symptom Matching Approach
+#             disease_scores = {disease: 0 for disease in diseases.keys()}
+#             for disease, symptoms in diseases.items():
+#                 matches = sum(symptom in selected_symptoms for symptom in symptoms)
+#                 disease_scores[disease] = matches / len(symptoms)  # Normalize by symptom count
+
+#             # Normalize to percentage
+#             symptom_match_scores = {d: round(score * 100, 2) for d, score in disease_scores.items()}
+
+#             # 2️⃣ AI Model Prediction
+#             ai_results = classifier(user_input, list(diseases.keys()))
+#             ai_scores = {ai_results["labels"][i]: round(ai_results["scores"][i] * 100, 2) for i in range(len(ai_results["labels"]))}
+
+#             # 3️⃣ Hybrid Score Calculation (Average of Both Scores)
+#             final_scores = {}
+#             for disease in diseases.keys():
+#                 symptom_score = symptom_match_scores.get(disease, 0)
+#                 ai_score = ai_scores.get(disease, 0)
+#                 final_scores[disease] = round((symptom_score + ai_score) / 2, 2)  # Averaging
+
+#             # Sort by final score
+#             sorted_final_scores = sorted(final_scores.items(), key=lambda x: x[1], reverse=True)
+
+#             # Display results
+#             st.write("### 🔬 Possible Conditions (Hybrid Model Prediction):")
+#             for disease, score in sorted_final_scores:
+#                 if score > 0:
+#                     st.write(f"🩺 {disease}: {score}% match")
+#         else:
+#             st.write("⚠️ Please select at least one symptom.")
+
+
+import torch
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+
+
+if selected == "Mental-Analysis":
+    # Load the Hugging Face model
+    model_name = "mental/mental-roberta-base"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+    # Sidebar with title and markdown
+    st.sidebar.title("🧠 Mental Health Analysis")
+    st.sidebar.markdown("""
+    Analyze mental health symptoms using a **pre-trained AI model**.  
+    This tool predicts **Depression and Anxiety** based on text input.
+    """)
+
+    # Main content
+    st.title("🔬 Mental Health Text Analysis")
+    st.markdown("Enter a description of your mental state, and the AI will predict possible conditions.")
+
+    # User input
+    user_input = st.text_area("Describe your symptoms (e.g., 'I feel hopeless and anxious all the time.'):")
+    
+    if st.button("Analyze"):
+        if user_input:
+            # Tokenize input
+            inputs = tokenizer(user_input, return_tensors="pt", truncation=True, padding=True)
+
+            # Get raw logits from the model
+            with torch.no_grad():
+                outputs = model(**inputs)
+            logits = outputs.logits
+
+            # Apply sigmoid activation to get independent probabilities
+            probs = torch.sigmoid(logits).squeeze().tolist()
+
+            # Map to labels
+            label_mapping = {
+                0: "Depression",
+                1: "Anxiety"
+            }
+            predictions = {label_mapping[i]: round(probs[i] * 100, 2) for i in range(len(probs))}
+
+            # Display predictions
+            st.write("### Predictions:")
+            for label, score in predictions.items():
+                st.write(f"🩺 **{label}**: {score}% confidence")
+
+            # Sort for better visualization
+            sorted_labels = sorted(predictions.keys(), key=lambda x: predictions[x], reverse=True)
+            sorted_scores = [predictions[label] for label in sorted_labels]
+
+            # Plot using Seaborn
+            fig, ax = plt.subplots(figsize=(4, 2.5))  # Compact size
+            sns.barplot(x=sorted_scores, y=sorted_labels, palette="coolwarm", ax=ax)
+
+            # Labels & title
+            ax.set_xlabel("Risk Probability (%)")
+            ax.set_title("Mental Health Risk Assessment")
+            ax.set_xlim(0, 100)
+            
+            # Add percentages inside bars
+            for i, (score, label) in enumerate(zip(sorted_scores, sorted_labels)):
+                ax.text(score - 5, i, f"{score}%", va='center', ha='right', color='white', fontsize=10, fontweight='bold')
+
+            # Display the chart in a single column
+            st.pyplot(fig)
+
+
+if selected == 'Sleep Health Analysis':
+    st.title("🌙 Sleep Health Analysis")
+    st.image("https://cdn-icons-png.flaticon.com/512/1205/1205526.png", width=100)
+    
+    st.markdown("""
+    This model predicts the likelihood of **Sleep Disorders** based on various health factors.  
+    Enter your details and click **"Sleep Health Test Result"** to get the prediction.
+    """)
+
+    # Load models
+    try:
+        sleep_model = pickle.load(open('sleep_health/svc_model.pkl', 'rb'))
+        scaler = pickle.load(open('sleep_health/scaler.pkl', 'rb'))
+        label_encoder = pickle.load(open('sleep_health/label_encoders.pkl', 'rb'))
+    except FileNotFoundError:
+        st.error("Error: Model files not found. Please upload the model files.")
+        st.stop()
+
+    # Input fields for user data
+    col1, col2 = st.columns(2)
+
+    with col1:
+        gender = st.selectbox('Gender', ['Male', 'Female'], key='gender_sleep')
+        age = st.slider("Age", min_value=27, max_value=59, value=35, 
+                      help="Age range in dataset: 27-59 years", key='age_sleep')
+        occupation = st.selectbox("Occupation", 
+            ['Software Engineer', 'Doctor', 'Sales Representative', 'Teacher', 'Business',
+             'Scientist', 'Accountant', 'Engineer'], key='occupation_sleep')
+        sleep_duration = st.slider("Sleep Duration (hours)", 
+            min_value=5.8, max_value=8.5, value=6.5, step=0.1,
+            help="Dataset range: 5.8-8.5 hours", key='sleep_duration')
+        quality_of_sleep = st.slider('Quality of Sleep', 
+            min_value=4, max_value=9, value=6,
+            help="Higher is better (4-9 scale)", key='quality_sleep')
+        physical_activity_level = st.slider('Physical Activity Level (minutes/day)', 
+            min_value=30, max_value=90, value=45,
+            help="Physical activity in minutes per day", key='activity_sleep')
+
+    with col2:
+        stress_level = st.slider('Stress Level', 
+            min_value=3, max_value=8, value=6, 
+            help="Higher values indicate higher stress (3-8 scale)", key='stress_sleep')
+        bmi_category = st.selectbox("BMI Category", 
+            ["Normal", "Overweight", "Obese"], key='bmi_sleep')
+        
+        # For blood pressure, let's use two separate inputs for systolic/diastolic
+        col2a, col2b = st.columns(2)
+        with col2a:
+            systolic = st.slider("Blood Pressure (Systolic)", 
+                min_value=110, max_value=140, value=125, key='bp_sys_sleep')
+        with col2b:
+            diastolic = st.slider("Diastolic", 
+                min_value=70, max_value=95, value=80, key='bp_dia_sleep')
+        
+        blood_pressure = f"{systolic}/{diastolic}"
+        
+        heart_rate = st.slider("Heart Rate (bpm)", 
+            min_value=65, max_value=86, value=75, 
+            help="Normal range: 60-100 bpm", key='hr_sleep')
+        daily_steps = st.slider("Daily Steps", 
+            min_value=3000, max_value=10000, value=6000, step=500,
+            help="Recommended: 7,000-10,000 steps/day", key='steps_sleep')
+
+    # Create a button to trigger prediction
+    if st.button('Sleep Health Test Result', key='sleep_test_button'):
+        try:
+            # Prepare input data
+            input_data = {
+                'Gender': gender,
+                'Age': age,
+                'Occupation': occupation,
+                'Sleep Duration': sleep_duration,
+                'Quality of Sleep': quality_of_sleep,
+                'Physical Activity Level': physical_activity_level,
+                'Stress Level': stress_level,
+                'BMI Category': bmi_category,
+                'Blood Pressure': blood_pressure,
+                'Heart Rate': heart_rate,
+                'Daily Steps': daily_steps
+            }
+            
+            # Process and predict
+            df = pd.DataFrame([input_data])
+            
+            # Apply label encoding to categorical features
+            for col, encoder in label_encoder.items():
+                if col in df.columns:
+                    df[col] = encoder.transform([df[col].iloc[0]])[0]
+            
+            # Feature Engineering and Preprocessing
+            columns_to_drop = ["Physical Activity Level", "Person ID"]
+            for col in columns_to_drop:
+                if col in df.columns:
+                    df = df.drop(columns=[col])
+            
+            # Create dummy variables with the correct column names expected by the model
+            df = pd.get_dummies(df)
+            
+            # Get the expected feature names from the model
+            # You might need to store these feature names during training
+            expected_features = [
+                'Age', 'Sleep Duration', 'Quality of Sleep', 'Stress Level',
+                'Heart Rate', 'Daily Steps', 'Gender_Female', 'Gender_Male',
+                'Occupation_Accountant', 'Occupation_Business', 'Occupation_Doctor',
+                'Occupation_Engineer', 'Occupation_Sales Representative', 
+                'Occupation_Scientist', 'Occupation_Software Engineer', 'Occupation_Teacher',
+                'BMI Category_Normal', 'BMI Category_Obese', 'BMI Category_Overweight',
+                'Blood Pressure_110/70', 'Blood Pressure_120/80', 'Blood Pressure_125/80',
+                'Blood Pressure_130/85', 'Blood Pressure_140/90'
+            ]
+            
+            # Create a DataFrame with expected features filled with zeros
+            prediction_df = pd.DataFrame(0, index=[0], columns=expected_features)
+            
+            # Fill in the values from our current DataFrame
+            for col in df.columns:
+                if col in prediction_df.columns:
+                    prediction_df[col] = df[col].values
+                    
+            # Scale with proper error handling
+            with st.spinner("⏳ Predicting... Please wait..."):
+                time.sleep(2)
+                # Use the properly formatted DataFrame
+                prediction = sleep_model.predict(prediction_df)
+            
+            # Display result
+            result = "🛑 High risk of sleep disorder" if prediction[0] == 1 else "✅ Low risk of sleep disorder"
+            if prediction[0] == 0:
+                st.balloons()
+            st.success(result)
+            
+            # Show risk factors based on input
+            st.subheader("Risk Factor Analysis")
+            risk_factors = []
+            
+            if sleep_duration < 6.0:
+                risk_factors.append("⚠️ Low sleep duration (less than 6 hours)")
+            if quality_of_sleep < 6:
+                risk_factors.append("⚠️ Poor sleep quality")
+            if stress_level > 6:
+                risk_factors.append("⚠️ High stress levels")
+            if bmi_category in ["Overweight", "Obese"]:
+                risk_factors.append(f"⚠️ {bmi_category} BMI category")
+            if int(systolic) > 130 or int(diastolic) > 85:
+                risk_factors.append("⚠️ Elevated blood pressure")
+            if heart_rate > 80:
+                risk_factors.append("⚠️ Elevated heart rate")
+            if daily_steps < 5000:
+                risk_factors.append("⚠️ Low daily activity (steps)")
+                
+            if risk_factors:
+                st.markdown("##### Potential Risk Factors:")
+                for factor in risk_factors:
+                    st.markdown(factor)
+            else:
+                st.markdown("✅ No significant risk factors identified.")
+
+        except Exception as e:
+            st.error(f"❌ Error: {e}")
+
+
+
+
+if selected=='Hypertension Prediction':
+    st.title("Hypertension Risk Prediction App")
+    st.markdown("This application uses an Extra Trees Classifier model to predict hypertension risk based on patient health data.")
+
+    # Load the model and scaler
+    try:
+        hypertension_model = pickle.load(open('hypertension\extratrees_model.pkl', 'rb'))
+        hypertension_scaler = pickle.load(open('hypertension\scaler.pkl', 'rb'))
+        st.success("Model and scaler loaded successfully!")
+    except Exception as e:
+        st.error(f"Error loading model or scaler: {e}")
+        st.info("Please check that model and scaler files are in the correct location.")
+        st.warning("Expected path: 'hypertension/extratrees_model.pkl' and 'hypertension/scaler.pkl'")
+
+    # Define input section
+    st.subheader("Patient Information")
+
+    # Create two columns for input layout
+    col1, col2 = st.columns(2)
+
+    with col1:
+        male = st.radio("Gender", options=[0, 1], format_func=lambda x: "Female" if x == 0 else "Male")
+        age = st.slider("Age", min_value=32, max_value=70, value=49, help="Patient's age (32-70 years)")
+        cigs_per_day = st.slider("Cigarettes Per Day", min_value=0.0, max_value=70.0, value=0.0, step=1.0)
+        bp_meds = st.radio("On Blood Pressure Medication", options=[0.0, 1.0], format_func=lambda x: "No" if x == 0.0 else "Yes")
+        tot_chol = st.slider("Total Cholesterol", min_value=107.0, max_value=500.0, value=234.0, step=1.0, help="mg/dL")
+
+    with col2:
+        sys_bp = st.slider("Systolic Blood Pressure", min_value=83.5, max_value=295.0, value=128.0, step=0.5, help="mmHg")
+        dia_bp = st.slider("Diastolic Blood Pressure", min_value=48.0, max_value=142.5, value=82.0, step=0.5, help="mmHg")
+        bmi = st.slider("BMI", min_value=15.54, max_value=56.80, value=25.40, step=0.01)
+        heart_rate = st.slider("Heart Rate", min_value=44.0, max_value=143.0, value=75.0, step=1.0, help="beats per minute")
+        glucose = st.slider("Glucose", min_value=40.0, max_value=394.0, value=78.0, step=1.0, help="mg/dL")
+
+    # Prediction button
+    predict_button = st.button("Predict Hypertension Risk")
+
+    if predict_button:
+        # Create input dataframe
+        input_data = pd.DataFrame({
+            'male': [male],
+            'age': [age],
+            'cigsPerDay': [cigs_per_day],
+            'BPMeds': [bp_meds],
+            'totChol': [tot_chol],
+            'sysBP': [sys_bp],
+            'diaBP': [dia_bp],
+            'BMI': [bmi],
+            'heartRate': [heart_rate],
+            'glucose': [glucose]
+        })
+        
+        # Display input data
+        st.subheader("Input Data:")
+        st.dataframe(input_data)
+        
+        # Identify numerical columns to scale
+        num_cols = ['age', 'cigsPerDay', 'totChol', 'sysBP', 'diaBP', 'BMI', 'heartRate', 'glucose']
+        
+        try:
+            # Scale the numerical features
+            input_data[num_cols] = hypertension_scaler.transform(input_data[num_cols])
+            
+            # Make prediction
+            prediction = hypertension_model.predict(input_data)[0]
+            prediction_prob = hypertension_model.predict_proba(input_data)[0]
+            
+            # Display prediction results
+            st.subheader("Prediction Result:")
+            
+            # Create columns for results
+            res_col1, res_col2 = st.columns(2)
+            
+            with res_col1:
+                if prediction == 0:
+                    st.success("✅ Low Risk of Hypertension")
+                else:
+                    st.error("🚨 High Risk of Hypertension")
+            
+            with res_col2:
+                # Visualization
+                st.write(f"Probability of Low Risk: {prediction_prob[0]:.2f}")
+                st.write(f"Probability of High Risk: {prediction_prob[1]:.2f}")
+                
+                # Add progress bar
+                st.progress(float(prediction_prob[1]))
+        
+        except Exception as e:
+            st.error(f"Error during prediction: {e}")
+        # st.info("Please check that all inputs are valid and within the expected ranges.")

asthama/asthma_dataset.csv

@@ -0,0 +1,301 @@
+Patient_ID,Age,Gender,Smoking_Status,Asthma_Diagnosis,Medication,Peak_Flow
+1,26,Female,Non-Smoker,Yes,Inhaler,175
+2,52,Female,Ex-Smoker,No,None,156
+3,56,Female,Ex-Smoker,Yes,Inhaler,236
+4,55,Male,Current Smoker,Yes,Controller Medication,378
+5,43,Female,Current Smoker,No,None,159
+6,23,Male,Non-Smoker,No,None,376
+7,33,Female,Current Smoker,No,None,296
+8,20,Male,Current Smoker,Yes,Controller Medication,322
+9,36,Male,Current Smoker,No,None,175
+10,27,Male,Ex-Smoker,No,None,264
+11,34,Female,Ex-Smoker,No,None,240
+12,43,Male,Current Smoker,No,None,354
+13,51,Female,Non-Smoker,Yes,Inhaler,171
+14,54,Male,Non-Smoker,Yes,Inhaler,327
+15,22,Female,Current Smoker,Yes,Inhaler,304
+16,53,Female,Non-Smoker,Yes,Controller Medication,336
+17,39,Female,Current Smoker,Yes,Controller Medication,307
+18,34,Male,Non-Smoker,Yes,Controller Medication,372
+19,51,Male,Current Smoker,No,None,239
+20,37,Male,Non-Smoker,Yes,Controller Medication,309
+21,52,Female,Ex-Smoker,No,None,323
+22,24,Female,Non-Smoker,No,None,160
+23,56,Female,Ex-Smoker,Yes,Controller Medication,286
+24,23,Female,Current Smoker,Yes,Inhaler,282
+25,58,Male,Non-Smoker,No,None,248
+26,27,Male,Ex-Smoker,No,None,325
+27,41,Male,Non-Smoker,No,None,233
+28,27,Male,Current Smoker,No,None,346
+29,63,Male,Current Smoker,No,None,379
+30,25,Female,Current Smoker,Yes,Inhaler,207
+31,42,Male,Non-Smoker,Yes,Inhaler,168
+32,18,Female,Non-Smoker,No,None,200
+33,22,Female,Ex-Smoker,Yes,Inhaler,323
+34,54,Male,Non-Smoker,Yes,Controller Medication,386
+35,22,Female,Ex-Smoker,Yes,Controller Medication,257
+36,34,Female,Current Smoker,Yes,Controller Medication,349
+37,20,Male,Current Smoker,No,None,273
+38,43,Male,Current Smoker,No,None,260
+39,19,Male,Non-Smoker,No,None,249
+40,65,Female,Non-Smoker,Yes,Controller Medication,203
+41,22,Female,Current Smoker,No,None,384
+42,58,Female,Current Smoker,Yes,Inhaler,253
+43,59,Male,Current Smoker,Yes,Inhaler,383
+44,54,Male,Ex-Smoker,Yes,Controller Medication,202
+45,57,Female,Ex-Smoker,Yes,Inhaler,310
+46,45,Male,Non-Smoker,No,None,287
+47,62,Female,Ex-Smoker,No,None,348
+48,39,Male,Non-Smoker,Yes,Controller Medication,230
+49,43,Female,Non-Smoker,No,None,388
+50,20,Male,Current Smoker,No,None,331
+51,59,Female,Non-Smoker,No,None,288
+52,21,Female,Non-Smoker,No,None,378
+53,58,Male,Current Smoker,No,None,284
+54,23,Male,Current Smoker,Yes,Controller Medication,205
+55,48,Male,Current Smoker,No,None,352
+56,54,Female,Ex-Smoker,Yes,Inhaler,383
+57,47,Male,Ex-Smoker,No,None,267
+58,50,Male,Non-Smoker,No,None,269
+59,64,Female,Ex-Smoker,No,None,398
+60,52,Female,Ex-Smoker,No,None,259
+61,38,Male,Ex-Smoker,No,None,307
+62,28,Male,Ex-Smoker,No,None,258
+63,46,Male,Non-Smoker,Yes,Controller Medication,194
+64,49,Male,Ex-Smoker,No,None,157
+65,60,Male,Ex-Smoker,Yes,Inhaler,207
+66,47,Male,Current Smoker,Yes,Controller Medication,382
+67,24,Female,Current Smoker,Yes,Controller Medication,151
+68,43,Male,Non-Smoker,No,None,321
+69,41,Female,Non-Smoker,Yes,Controller Medication,260
+70,49,Male,Ex-Smoker,No,None,284
+71,50,Female,Non-Smoker,Yes,Controller Medication,176
+72,20,Male,Non-Smoker,No,None,248
+73,58,Female,Non-Smoker,Yes,Inhaler,270
+74,28,Female,Current Smoker,Yes,Controller Medication,389
+75,39,Male,Non-Smoker,No,None,357
+76,65,Male,Non-Smoker,Yes,Inhaler,280
+77,43,Female,Ex-Smoker,Yes,Inhaler,397
+78,32,Female,Current Smoker,Yes,Controller Medication,200
+79,63,Female,Non-Smoker,Yes,Controller Medication,212
+80,58,Female,Ex-Smoker,No,None,300
+81,22,Female,Ex-Smoker,Yes,Controller Medication,294
+82,37,Male,Ex-Smoker,Yes,Inhaler,358
+83,52,Female,Ex-Smoker,Yes,Inhaler,198
+84,51,Male,Current Smoker,Yes,Controller Medication,263
+85,25,Female,Current Smoker,Yes,Controller Medication,200
+86,30,Male,Ex-Smoker,No,None,159
+87,53,Male,Ex-Smoker,No,None,152
+88,38,Female,Ex-Smoker,No,None,267
+89,45,Male,Current Smoker,Yes,Inhaler,237
+90,38,Female,Ex-Smoker,No,None,286
+91,23,Male,Non-Smoker,No,None,363
+92,36,Female,Non-Smoker,No,None,380
+93,38,Female,Ex-Smoker,Yes,Controller Medication,305
+94,34,Male,Current Smoker,No,None,218
+95,29,Female,Non-Smoker,No,None,185
+96,23,Male,Non-Smoker,No,None,181
+97,36,Male,Ex-Smoker,No,None,336
+98,46,Female,Non-Smoker,Yes,Inhaler,262
+99,34,Female,Non-Smoker,Yes,Inhaler,188
+100,23,Male,Ex-Smoker,Yes,Inhaler,384
+101,45,Male,Ex-Smoker,Yes,Controller Medication,210
+102,33,Male,Current Smoker,No,None,338
+103,27,Male,Non-Smoker,No,None,294
+104,34,Female,Current Smoker,No,None,301
+105,27,Male,Ex-Smoker,No,None,322
+106,47,Female,Non-Smoker,Yes,Controller Medication,263
+107,21,Male,Ex-Smoker,Yes,Inhaler,308
+108,44,Female,Non-Smoker,Yes,Inhaler,377
+109,44,Male,Non-Smoker,Yes,Inhaler,289
+110,52,Male,Ex-Smoker,No,None,197
+111,26,Male,Current Smoker,Yes,Inhaler,337
+112,48,Male,Current Smoker,Yes,Controller Medication,346
+113,27,Male,Current Smoker,Yes,Controller Medication,385
+114,24,Female,Non-Smoker,No,None,281
+115,34,Female,Ex-Smoker,No,None,358
+116,49,Male,Current Smoker,No,None,212
+117,65,Male,Non-Smoker,No,None,268
+118,30,Female,Non-Smoker,Yes,Inhaler,372
+119,62,Female,Non-Smoker,No,None,377
+120,45,Female,Current Smoker,Yes,Controller Medication,383
+121,58,Female,Non-Smoker,Yes,Controller Medication,316
+122,48,Male,Non-Smoker,Yes,Inhaler,245
+123,63,Female,Ex-Smoker,Yes,Inhaler,162
+124,48,Male,Ex-Smoker,No,None,348
+125,51,Male,Non-Smoker,Yes,Inhaler,232
+126,26,Female,Current Smoker,No,None,184
+127,23,Female,Current Smoker,No,None,182
+128,27,Female,Ex-Smoker,No,None,215
+129,43,Female,Non-Smoker,No,None,276
+130,32,Female,Current Smoker,Yes,Inhaler,357
+131,23,Female,Non-Smoker,Yes,Controller Medication,390
+132,29,Female,Ex-Smoker,Yes,Controller Medication,295
+133,29,Male,Non-Smoker,No,None,376
+134,30,Female,Non-Smoker,No,None,261
+135,24,Male,Ex-Smoker,No,None,254
+136,27,Female,Current Smoker,No,None,226
+137,52,Male,Current Smoker,No,None,363
+138,34,Female,Current Smoker,No,None,381
+139,29,Female,Current Smoker,Yes,Controller Medication,162
+140,52,Female,Non-Smoker,Yes,Controller Medication,207
+141,24,Female,Non-Smoker,Yes,Controller Medication,280
+142,41,Male,Current Smoker,Yes,Controller Medication,150
+143,65,Male,Current Smoker,Yes,Inhaler,178
+144,33,Male,Ex-Smoker,No,None,290
+145,21,Male,Current Smoker,Yes,Controller Medication,376
+146,36,Male,Current Smoker,No,None,176
+147,59,Female,Ex-Smoker,Yes,Inhaler,392
+148,27,Male,Current Smoker,Yes,Inhaler,224
+149,46,Male,Non-Smoker,No,None,192
+150,38,Female,Non-Smoker,Yes,Inhaler,314
+151,62,Female,Current Smoker,Yes,Controller Medication,347
+152,30,Male,Ex-Smoker,No,None,203
+153,31,Female,Non-Smoker,No,None,351
+154,25,Female,Current Smoker,Yes,Inhaler,304
+155,45,Female,Current Smoker,Yes,Inhaler,181
+156,27,Female,Non-Smoker,Yes,Controller Medication,159
+157,42,Male,Ex-Smoker,No,None,302
+158,29,Male,Non-Smoker,No,None,332
+159,18,Male,Ex-Smoker,Yes,Inhaler,183
+160,18,Male,Ex-Smoker,Yes,Inhaler,375
+161,26,Female,Non-Smoker,No,None,351
+162,44,Female,Current Smoker,Yes,Controller Medication,234
+163,45,Male,Non-Smoker,No,None,400
+164,49,Female,Ex-Smoker,Yes,Controller Medication,327
+165,42,Female,Non-Smoker,Yes,Controller Medication,274
+166,59,Female,Ex-Smoker,No,None,178
+167,28,Female,Ex-Smoker,No,None,278
+168,34,Female,Current Smoker,No,None,334
+169,64,Male,Current Smoker,No,None,154
+170,41,Female,Current Smoker,No,None,253
+171,59,Female,Non-Smoker,Yes,Inhaler,274
+172,38,Male,Ex-Smoker,Yes,Controller Medication,364
+173,41,Female,Non-Smoker,No,None,199
+174,46,Female,Current Smoker,Yes,Inhaler,251
+175,37,Female,Ex-Smoker,Yes,Inhaler,248
+176,41,Female,Ex-Smoker,No,None,400
+177,61,Male,Non-Smoker,Yes,Controller Medication,175
+178,41,Female,Ex-Smoker,No,None,169
+179,21,Male,Ex-Smoker,No,None,377
+180,25,Female,Current Smoker,Yes,Controller Medication,382
+181,33,Female,Current Smoker,Yes,Inhaler,304
+182,27,Male,Non-Smoker,Yes,Controller Medication,230
+183,41,Female,Current Smoker,Yes,Controller Medication,375
+184,39,Female,Ex-Smoker,Yes,Controller Medication,245
+185,22,Male,Current Smoker,No,None,277
+186,28,Male,Non-Smoker,Yes,Controller Medication,320
+187,24,Female,Non-Smoker,Yes,Controller Medication,224
+188,46,Female,Non-Smoker,Yes,Inhaler,389
+189,29,Male,Ex-Smoker,No,None,383
+190,55,Male,Current Smoker,Yes,Inhaler,323
+191,35,Female,Current Smoker,Yes,Controller Medication,329
+192,29,Male,Current Smoker,No,None,372
+193,27,Male,Ex-Smoker,Yes,Inhaler,217
+194,23,Male,Current Smoker,Yes,Inhaler,333
+195,49,Female,Ex-Smoker,Yes,Inhaler,241
+196,37,Female,Ex-Smoker,Yes,Controller Medication,261
+197,26,Female,Non-Smoker,No,None,337
+198,52,Female,Non-Smoker,Yes,Inhaler,189
+199,20,Female,Non-Smoker,Yes,Inhaler,373
+200,29,Female,Non-Smoker,No,None,152
+201,60,Male,Current Smoker,Yes,Controller Medication,297
+202,65,Male,Current Smoker,Yes,Inhaler,391
+203,64,Female,Current Smoker,No,None,232
+204,20,Male,Non-Smoker,No,None,196
+205,59,Female,Current Smoker,Yes,Controller Medication,268
+206,50,Male,Ex-Smoker,No,None,161
+207,59,Female,Current Smoker,Yes,Controller Medication,248
+208,20,Female,Current Smoker,Yes,Inhaler,172
+209,48,Male,Current Smoker,Yes,Inhaler,282
+210,47,Male,Non-Smoker,No,None,254
+211,65,Female,Ex-Smoker,No,None,395
+212,23,Male,Current Smoker,Yes,Inhaler,337
+213,48,Male,Ex-Smoker,No,None,328
+214,65,Male,Ex-Smoker,Yes,Inhaler,397
+215,54,Female,Current Smoker,No,None,334
+216,38,Male,Ex-Smoker,No,None,220
+217,24,Female,Current Smoker,No,None,271
+218,60,Female,Ex-Smoker,No,None,228
+219,27,Male,Ex-Smoker,Yes,Controller Medication,332
+220,21,Female,Non-Smoker,No,None,231
+221,52,Female,Current Smoker,Yes,Controller Medication,175
+222,44,Male,Non-Smoker,Yes,Controller Medication,378
+223,22,Female,Current Smoker,No,None,320
+224,30,Female,Ex-Smoker,No,None,277
+225,19,Female,Current Smoker,No,None,161
+226,30,Female,Current Smoker,Yes,Inhaler,264
+227,61,Female,Non-Smoker,No,None,385
+228,27,Female,Non-Smoker,No,None,375
+229,45,Male,Non-Smoker,Yes,Inhaler,398
+230,64,Male,Non-Smoker,Yes,Controller Medication,154
+231,29,Female,Non-Smoker,Yes,Controller Medication,296
+232,18,Male,Non-Smoker,Yes,Controller Medication,176
+233,56,Female,Ex-Smoker,No,None,301
+234,30,Male,Ex-Smoker,Yes,Controller Medication,351
+235,64,Female,Current Smoker,Yes,Controller Medication,218
+236,26,Female,Current Smoker,No,None,210
+237,45,Male,Non-Smoker,No,None,372
+238,45,Female,Non-Smoker,Yes,Inhaler,316
+239,64,Female,Non-Smoker,Yes,Inhaler,164
+240,64,Male,Non-Smoker,No,None,328
+241,58,Female,Current Smoker,Yes,Inhaler,315
+242,22,Male,Non-Smoker,Yes,Inhaler,204
+243,39,Male,Non-Smoker,Yes,Controller Medication,257
+244,45,Female,Non-Smoker,Yes,Inhaler,151
+245,33,Male,Non-Smoker,Yes,Inhaler,352
+246,31,Female,Non-Smoker,No,None,226
+247,63,Female,Current Smoker,No,None,310
+248,63,Female,Current Smoker,No,None,289
+249,46,Female,Non-Smoker,Yes,Controller Medication,218
+250,54,Female,Non-Smoker,Yes,Inhaler,341
+251,21,Female,Current Smoker,Yes,Controller Medication,217
+252,32,Male,Current Smoker,No,None,360
+253,54,Female,Non-Smoker,Yes,Controller Medication,184
+254,58,Female,Current Smoker,No,None,283
+255,19,Female,Current Smoker,No,None,213
+256,42,Male,Current Smoker,No,None,265
+257,40,Male,Current Smoker,No,None,231
+258,43,Male,Non-Smoker,Yes,Controller Medication,349
+259,40,Male,Ex-Smoker,No,None,371
+260,51,Male,Non-Smoker,Yes,Controller Medication,272
+261,32,Female,Ex-Smoker,Yes,Inhaler,334
+262,28,Male,Current Smoker,Yes,Inhaler,170
+263,59,Male,Ex-Smoker,Yes,Inhaler,212
+264,47,Female,Current Smoker,Yes,Controller Medication,230
+265,19,Male,Ex-Smoker,Yes,Controller Medication,384
+266,32,Female,Ex-Smoker,No,None,177
+267,43,Female,Ex-Smoker,Yes,Controller Medication,206
+268,50,Male,Non-Smoker,Yes,Controller Medication,330
+269,29,Male,Non-Smoker,No,None,225
+270,29,Female,Non-Smoker,Yes,Controller Medication,260
+271,48,Male,Current Smoker,Yes,Inhaler,211
+272,29,Male,Ex-Smoker,No,None,270
+273,35,Female,Non-Smoker,No,None,260
+274,51,Male,Ex-Smoker,No,None,332
+275,65,Female,Non-Smoker,Yes,Controller Medication,229
+276,63,Female,Ex-Smoker,No,None,381
+277,31,Female,Non-Smoker,No,None,188
+278,63,Female,Current Smoker,Yes,Controller Medication,387
+279,42,Female,Non-Smoker,No,None,178
+280,44,Male,Ex-Smoker,Yes,Controller Medication,198
+281,59,Male,Non-Smoker,No,None,187
+282,18,Male,Non-Smoker,Yes,Inhaler,243
+283,53,Male,Ex-Smoker,No,None,286
+284,49,Male,Ex-Smoker,Yes,Controller Medication,300
+285,20,Male,Current Smoker,Yes,Controller Medication,340
+286,30,Male,Ex-Smoker,No,None,293
+287,21,Female,Current Smoker,Yes,Controller Medication,253
+288,21,Female,Ex-Smoker,Yes,Inhaler,272
+289,21,Female,Non-Smoker,Yes,Controller Medication,262
+290,22,Male,Ex-Smoker,No,None,400
+291,47,Female,Ex-Smoker,No,None,362
+292,19,Male,Ex-Smoker,Yes,Controller Medication,287
+293,28,Female,Non-Smoker,Yes,Inhaler,350
+294,36,Female,Current Smoker,No,None,345
+295,49,Male,Non-Smoker,Yes,Inhaler,367
+296,25,Female,Current Smoker,No,None,283
+297,20,Male,Ex-Smoker,No,None,202
+298,25,Female,Non-Smoker,No,None,208
+299,18,Male,Ex-Smoker,No,None,175
+300,59,Female,Current Smoker,No,None,332

asthama/model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d7e270f37e7a4fd5d536a0b6c96a233cbff0397e95aba0cfb6c91eaf107ba53
+size 57844

asthama/preprocessor.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a961c4e191efaaacd8b0e7018371aaf206956cf1ccf635fcf00263c32f0ef18
+size 4251

cardio_vascular/xgboost_cardiovascular_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46dd4efb618b2c3dd2bd0c0988f6aec3d0c43db28bc71c766637ca99c1995f5a
+size 769691

data_csv/Sleep_health_and_lifestyle_dataset.csv

@@ -0,0 +1,375 @@
+Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder
+1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None
+2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia
+7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia
+8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea
+18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea
+19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia
+20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea
+32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia
+33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None
+34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea
+51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None
+68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia
+69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia
+96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea
+105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea
+106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia
+107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None
+108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None
+127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea
+146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia
+148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia
+149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None
+150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia
+167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None
+168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None
+205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None
+206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea
+220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea
+221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+224,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+225,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+226,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+227,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+228,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+230,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+231,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+232,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+233,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+235,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+238,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+239,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+240,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+241,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+242,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+243,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia
+249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,None
+250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,None
+251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+257,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,None
+265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,None
+269,Female,49,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia
+280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+281,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,None
+282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+283,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+284,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+285,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+286,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+287,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+288,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+289,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+290,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+291,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+292,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+293,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+294,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+296,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,None
+304,Female,51,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia
+317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+353,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+354,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+355,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+356,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+357,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+358,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+359,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,None
+360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+365,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+366,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+367,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+368,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+369,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+370,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+371,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+372,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+374,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea

data_csv/asthma_dataset.csv

@@ -0,0 +1,301 @@
+Patient_ID,Age,Gender,Smoking_Status,Asthma_Diagnosis,Medication,Peak_Flow
+1,26,Female,Non-Smoker,Yes,Inhaler,175
+2,52,Female,Ex-Smoker,No,None,156
+3,56,Female,Ex-Smoker,Yes,Inhaler,236
+4,55,Male,Current Smoker,Yes,Controller Medication,378
+5,43,Female,Current Smoker,No,None,159
+6,23,Male,Non-Smoker,No,None,376
+7,33,Female,Current Smoker,No,None,296
+8,20,Male,Current Smoker,Yes,Controller Medication,322
+9,36,Male,Current Smoker,No,None,175
+10,27,Male,Ex-Smoker,No,None,264
+11,34,Female,Ex-Smoker,No,None,240
+12,43,Male,Current Smoker,No,None,354
+13,51,Female,Non-Smoker,Yes,Inhaler,171
+14,54,Male,Non-Smoker,Yes,Inhaler,327
+15,22,Female,Current Smoker,Yes,Inhaler,304
+16,53,Female,Non-Smoker,Yes,Controller Medication,336
+17,39,Female,Current Smoker,Yes,Controller Medication,307
+18,34,Male,Non-Smoker,Yes,Controller Medication,372
+19,51,Male,Current Smoker,No,None,239
+20,37,Male,Non-Smoker,Yes,Controller Medication,309
+21,52,Female,Ex-Smoker,No,None,323
+22,24,Female,Non-Smoker,No,None,160
+23,56,Female,Ex-Smoker,Yes,Controller Medication,286
+24,23,Female,Current Smoker,Yes,Inhaler,282
+25,58,Male,Non-Smoker,No,None,248
+26,27,Male,Ex-Smoker,No,None,325
+27,41,Male,Non-Smoker,No,None,233
+28,27,Male,Current Smoker,No,None,346
+29,63,Male,Current Smoker,No,None,379
+30,25,Female,Current Smoker,Yes,Inhaler,207
+31,42,Male,Non-Smoker,Yes,Inhaler,168
+32,18,Female,Non-Smoker,No,None,200
+33,22,Female,Ex-Smoker,Yes,Inhaler,323
+34,54,Male,Non-Smoker,Yes,Controller Medication,386
+35,22,Female,Ex-Smoker,Yes,Controller Medication,257
+36,34,Female,Current Smoker,Yes,Controller Medication,349
+37,20,Male,Current Smoker,No,None,273
+38,43,Male,Current Smoker,No,None,260
+39,19,Male,Non-Smoker,No,None,249
+40,65,Female,Non-Smoker,Yes,Controller Medication,203
+41,22,Female,Current Smoker,No,None,384
+42,58,Female,Current Smoker,Yes,Inhaler,253
+43,59,Male,Current Smoker,Yes,Inhaler,383
+44,54,Male,Ex-Smoker,Yes,Controller Medication,202
+45,57,Female,Ex-Smoker,Yes,Inhaler,310
+46,45,Male,Non-Smoker,No,None,287
+47,62,Female,Ex-Smoker,No,None,348
+48,39,Male,Non-Smoker,Yes,Controller Medication,230
+49,43,Female,Non-Smoker,No,None,388
+50,20,Male,Current Smoker,No,None,331
+51,59,Female,Non-Smoker,No,None,288
+52,21,Female,Non-Smoker,No,None,378
+53,58,Male,Current Smoker,No,None,284
+54,23,Male,Current Smoker,Yes,Controller Medication,205
+55,48,Male,Current Smoker,No,None,352
+56,54,Female,Ex-Smoker,Yes,Inhaler,383
+57,47,Male,Ex-Smoker,No,None,267
+58,50,Male,Non-Smoker,No,None,269
+59,64,Female,Ex-Smoker,No,None,398
+60,52,Female,Ex-Smoker,No,None,259
+61,38,Male,Ex-Smoker,No,None,307
+62,28,Male,Ex-Smoker,No,None,258
+63,46,Male,Non-Smoker,Yes,Controller Medication,194
+64,49,Male,Ex-Smoker,No,None,157
+65,60,Male,Ex-Smoker,Yes,Inhaler,207
+66,47,Male,Current Smoker,Yes,Controller Medication,382
+67,24,Female,Current Smoker,Yes,Controller Medication,151
+68,43,Male,Non-Smoker,No,None,321
+69,41,Female,Non-Smoker,Yes,Controller Medication,260
+70,49,Male,Ex-Smoker,No,None,284
+71,50,Female,Non-Smoker,Yes,Controller Medication,176
+72,20,Male,Non-Smoker,No,None,248
+73,58,Female,Non-Smoker,Yes,Inhaler,270
+74,28,Female,Current Smoker,Yes,Controller Medication,389
+75,39,Male,Non-Smoker,No,None,357
+76,65,Male,Non-Smoker,Yes,Inhaler,280
+77,43,Female,Ex-Smoker,Yes,Inhaler,397
+78,32,Female,Current Smoker,Yes,Controller Medication,200
+79,63,Female,Non-Smoker,Yes,Controller Medication,212
+80,58,Female,Ex-Smoker,No,None,300
+81,22,Female,Ex-Smoker,Yes,Controller Medication,294
+82,37,Male,Ex-Smoker,Yes,Inhaler,358
+83,52,Female,Ex-Smoker,Yes,Inhaler,198
+84,51,Male,Current Smoker,Yes,Controller Medication,263
+85,25,Female,Current Smoker,Yes,Controller Medication,200
+86,30,Male,Ex-Smoker,No,None,159
+87,53,Male,Ex-Smoker,No,None,152
+88,38,Female,Ex-Smoker,No,None,267
+89,45,Male,Current Smoker,Yes,Inhaler,237
+90,38,Female,Ex-Smoker,No,None,286
+91,23,Male,Non-Smoker,No,None,363
+92,36,Female,Non-Smoker,No,None,380
+93,38,Female,Ex-Smoker,Yes,Controller Medication,305
+94,34,Male,Current Smoker,No,None,218
+95,29,Female,Non-Smoker,No,None,185
+96,23,Male,Non-Smoker,No,None,181
+97,36,Male,Ex-Smoker,No,None,336
+98,46,Female,Non-Smoker,Yes,Inhaler,262
+99,34,Female,Non-Smoker,Yes,Inhaler,188
+100,23,Male,Ex-Smoker,Yes,Inhaler,384
+101,45,Male,Ex-Smoker,Yes,Controller Medication,210
+102,33,Male,Current Smoker,No,None,338
+103,27,Male,Non-Smoker,No,None,294
+104,34,Female,Current Smoker,No,None,301
+105,27,Male,Ex-Smoker,No,None,322
+106,47,Female,Non-Smoker,Yes,Controller Medication,263
+107,21,Male,Ex-Smoker,Yes,Inhaler,308
+108,44,Female,Non-Smoker,Yes,Inhaler,377
+109,44,Male,Non-Smoker,Yes,Inhaler,289
+110,52,Male,Ex-Smoker,No,None,197
+111,26,Male,Current Smoker,Yes,Inhaler,337
+112,48,Male,Current Smoker,Yes,Controller Medication,346
+113,27,Male,Current Smoker,Yes,Controller Medication,385
+114,24,Female,Non-Smoker,No,None,281
+115,34,Female,Ex-Smoker,No,None,358
+116,49,Male,Current Smoker,No,None,212
+117,65,Male,Non-Smoker,No,None,268
+118,30,Female,Non-Smoker,Yes,Inhaler,372
+119,62,Female,Non-Smoker,No,None,377
+120,45,Female,Current Smoker,Yes,Controller Medication,383
+121,58,Female,Non-Smoker,Yes,Controller Medication,316
+122,48,Male,Non-Smoker,Yes,Inhaler,245
+123,63,Female,Ex-Smoker,Yes,Inhaler,162
+124,48,Male,Ex-Smoker,No,None,348
+125,51,Male,Non-Smoker,Yes,Inhaler,232
+126,26,Female,Current Smoker,No,None,184
+127,23,Female,Current Smoker,No,None,182
+128,27,Female,Ex-Smoker,No,None,215
+129,43,Female,Non-Smoker,No,None,276
+130,32,Female,Current Smoker,Yes,Inhaler,357
+131,23,Female,Non-Smoker,Yes,Controller Medication,390
+132,29,Female,Ex-Smoker,Yes,Controller Medication,295
+133,29,Male,Non-Smoker,No,None,376
+134,30,Female,Non-Smoker,No,None,261
+135,24,Male,Ex-Smoker,No,None,254
+136,27,Female,Current Smoker,No,None,226
+137,52,Male,Current Smoker,No,None,363
+138,34,Female,Current Smoker,No,None,381
+139,29,Female,Current Smoker,Yes,Controller Medication,162
+140,52,Female,Non-Smoker,Yes,Controller Medication,207
+141,24,Female,Non-Smoker,Yes,Controller Medication,280
+142,41,Male,Current Smoker,Yes,Controller Medication,150
+143,65,Male,Current Smoker,Yes,Inhaler,178
+144,33,Male,Ex-Smoker,No,None,290
+145,21,Male,Current Smoker,Yes,Controller Medication,376
+146,36,Male,Current Smoker,No,None,176
+147,59,Female,Ex-Smoker,Yes,Inhaler,392
+148,27,Male,Current Smoker,Yes,Inhaler,224
+149,46,Male,Non-Smoker,No,None,192
+150,38,Female,Non-Smoker,Yes,Inhaler,314
+151,62,Female,Current Smoker,Yes,Controller Medication,347
+152,30,Male,Ex-Smoker,No,None,203
+153,31,Female,Non-Smoker,No,None,351
+154,25,Female,Current Smoker,Yes,Inhaler,304
+155,45,Female,Current Smoker,Yes,Inhaler,181
+156,27,Female,Non-Smoker,Yes,Controller Medication,159
+157,42,Male,Ex-Smoker,No,None,302
+158,29,Male,Non-Smoker,No,None,332
+159,18,Male,Ex-Smoker,Yes,Inhaler,183
+160,18,Male,Ex-Smoker,Yes,Inhaler,375
+161,26,Female,Non-Smoker,No,None,351
+162,44,Female,Current Smoker,Yes,Controller Medication,234
+163,45,Male,Non-Smoker,No,None,400
+164,49,Female,Ex-Smoker,Yes,Controller Medication,327
+165,42,Female,Non-Smoker,Yes,Controller Medication,274
+166,59,Female,Ex-Smoker,No,None,178
+167,28,Female,Ex-Smoker,No,None,278
+168,34,Female,Current Smoker,No,None,334
+169,64,Male,Current Smoker,No,None,154
+170,41,Female,Current Smoker,No,None,253
+171,59,Female,Non-Smoker,Yes,Inhaler,274
+172,38,Male,Ex-Smoker,Yes,Controller Medication,364
+173,41,Female,Non-Smoker,No,None,199
+174,46,Female,Current Smoker,Yes,Inhaler,251
+175,37,Female,Ex-Smoker,Yes,Inhaler,248
+176,41,Female,Ex-Smoker,No,None,400
+177,61,Male,Non-Smoker,Yes,Controller Medication,175
+178,41,Female,Ex-Smoker,No,None,169
+179,21,Male,Ex-Smoker,No,None,377
+180,25,Female,Current Smoker,Yes,Controller Medication,382
+181,33,Female,Current Smoker,Yes,Inhaler,304
+182,27,Male,Non-Smoker,Yes,Controller Medication,230
+183,41,Female,Current Smoker,Yes,Controller Medication,375
+184,39,Female,Ex-Smoker,Yes,Controller Medication,245
+185,22,Male,Current Smoker,No,None,277
+186,28,Male,Non-Smoker,Yes,Controller Medication,320
+187,24,Female,Non-Smoker,Yes,Controller Medication,224
+188,46,Female,Non-Smoker,Yes,Inhaler,389
+189,29,Male,Ex-Smoker,No,None,383
+190,55,Male,Current Smoker,Yes,Inhaler,323
+191,35,Female,Current Smoker,Yes,Controller Medication,329
+192,29,Male,Current Smoker,No,None,372
+193,27,Male,Ex-Smoker,Yes,Inhaler,217
+194,23,Male,Current Smoker,Yes,Inhaler,333
+195,49,Female,Ex-Smoker,Yes,Inhaler,241
+196,37,Female,Ex-Smoker,Yes,Controller Medication,261
+197,26,Female,Non-Smoker,No,None,337
+198,52,Female,Non-Smoker,Yes,Inhaler,189
+199,20,Female,Non-Smoker,Yes,Inhaler,373
+200,29,Female,Non-Smoker,No,None,152
+201,60,Male,Current Smoker,Yes,Controller Medication,297
+202,65,Male,Current Smoker,Yes,Inhaler,391
+203,64,Female,Current Smoker,No,None,232
+204,20,Male,Non-Smoker,No,None,196
+205,59,Female,Current Smoker,Yes,Controller Medication,268
+206,50,Male,Ex-Smoker,No,None,161
+207,59,Female,Current Smoker,Yes,Controller Medication,248
+208,20,Female,Current Smoker,Yes,Inhaler,172
+209,48,Male,Current Smoker,Yes,Inhaler,282
+210,47,Male,Non-Smoker,No,None,254
+211,65,Female,Ex-Smoker,No,None,395
+212,23,Male,Current Smoker,Yes,Inhaler,337
+213,48,Male,Ex-Smoker,No,None,328
+214,65,Male,Ex-Smoker,Yes,Inhaler,397
+215,54,Female,Current Smoker,No,None,334
+216,38,Male,Ex-Smoker,No,None,220
+217,24,Female,Current Smoker,No,None,271
+218,60,Female,Ex-Smoker,No,None,228
+219,27,Male,Ex-Smoker,Yes,Controller Medication,332
+220,21,Female,Non-Smoker,No,None,231
+221,52,Female,Current Smoker,Yes,Controller Medication,175
+222,44,Male,Non-Smoker,Yes,Controller Medication,378
+223,22,Female,Current Smoker,No,None,320
+224,30,Female,Ex-Smoker,No,None,277
+225,19,Female,Current Smoker,No,None,161
+226,30,Female,Current Smoker,Yes,Inhaler,264
+227,61,Female,Non-Smoker,No,None,385
+228,27,Female,Non-Smoker,No,None,375
+229,45,Male,Non-Smoker,Yes,Inhaler,398
+230,64,Male,Non-Smoker,Yes,Controller Medication,154
+231,29,Female,Non-Smoker,Yes,Controller Medication,296
+232,18,Male,Non-Smoker,Yes,Controller Medication,176
+233,56,Female,Ex-Smoker,No,None,301
+234,30,Male,Ex-Smoker,Yes,Controller Medication,351
+235,64,Female,Current Smoker,Yes,Controller Medication,218
+236,26,Female,Current Smoker,No,None,210
+237,45,Male,Non-Smoker,No,None,372
+238,45,Female,Non-Smoker,Yes,Inhaler,316
+239,64,Female,Non-Smoker,Yes,Inhaler,164
+240,64,Male,Non-Smoker,No,None,328
+241,58,Female,Current Smoker,Yes,Inhaler,315
+242,22,Male,Non-Smoker,Yes,Inhaler,204
+243,39,Male,Non-Smoker,Yes,Controller Medication,257
+244,45,Female,Non-Smoker,Yes,Inhaler,151
+245,33,Male,Non-Smoker,Yes,Inhaler,352
+246,31,Female,Non-Smoker,No,None,226
+247,63,Female,Current Smoker,No,None,310
+248,63,Female,Current Smoker,No,None,289
+249,46,Female,Non-Smoker,Yes,Controller Medication,218
+250,54,Female,Non-Smoker,Yes,Inhaler,341
+251,21,Female,Current Smoker,Yes,Controller Medication,217
+252,32,Male,Current Smoker,No,None,360
+253,54,Female,Non-Smoker,Yes,Controller Medication,184
+254,58,Female,Current Smoker,No,None,283
+255,19,Female,Current Smoker,No,None,213
+256,42,Male,Current Smoker,No,None,265
+257,40,Male,Current Smoker,No,None,231
+258,43,Male,Non-Smoker,Yes,Controller Medication,349
+259,40,Male,Ex-Smoker,No,None,371
+260,51,Male,Non-Smoker,Yes,Controller Medication,272
+261,32,Female,Ex-Smoker,Yes,Inhaler,334
+262,28,Male,Current Smoker,Yes,Inhaler,170
+263,59,Male,Ex-Smoker,Yes,Inhaler,212
+264,47,Female,Current Smoker,Yes,Controller Medication,230
+265,19,Male,Ex-Smoker,Yes,Controller Medication,384
+266,32,Female,Ex-Smoker,No,None,177
+267,43,Female,Ex-Smoker,Yes,Controller Medication,206
+268,50,Male,Non-Smoker,Yes,Controller Medication,330
+269,29,Male,Non-Smoker,No,None,225
+270,29,Female,Non-Smoker,Yes,Controller Medication,260
+271,48,Male,Current Smoker,Yes,Inhaler,211
+272,29,Male,Ex-Smoker,No,None,270
+273,35,Female,Non-Smoker,No,None,260
+274,51,Male,Ex-Smoker,No,None,332
+275,65,Female,Non-Smoker,Yes,Controller Medication,229
+276,63,Female,Ex-Smoker,No,None,381
+277,31,Female,Non-Smoker,No,None,188
+278,63,Female,Current Smoker,Yes,Controller Medication,387
+279,42,Female,Non-Smoker,No,None,178
+280,44,Male,Ex-Smoker,Yes,Controller Medication,198
+281,59,Male,Non-Smoker,No,None,187
+282,18,Male,Non-Smoker,Yes,Inhaler,243
+283,53,Male,Ex-Smoker,No,None,286
+284,49,Male,Ex-Smoker,Yes,Controller Medication,300
+285,20,Male,Current Smoker,Yes,Controller Medication,340
+286,30,Male,Ex-Smoker,No,None,293
+287,21,Female,Current Smoker,Yes,Controller Medication,253
+288,21,Female,Ex-Smoker,Yes,Inhaler,272
+289,21,Female,Non-Smoker,Yes,Controller Medication,262
+290,22,Male,Ex-Smoker,No,None,400
+291,47,Female,Ex-Smoker,No,None,362
+292,19,Male,Ex-Smoker,Yes,Controller Medication,287
+293,28,Female,Non-Smoker,Yes,Inhaler,350
+294,36,Female,Current Smoker,No,None,345
+295,49,Male,Non-Smoker,Yes,Inhaler,367
+296,25,Female,Current Smoker,No,None,283
+297,20,Male,Ex-Smoker,No,None,202
+298,25,Female,Non-Smoker,No,None,208
+299,18,Male,Ex-Smoker,No,None,175
+300,59,Female,Current Smoker,No,None,332

data_csv/diabetes.csv

@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24,0.966,33,0
+11,138,76,0,0,33.2,0.42,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.39,56,1
+3,180,64,25,70,34,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
+9,171,110,24,240,45.4,0.721,54,1
+7,159,64,0,0,27.4,0.294,40,0
+0,180,66,39,0,42,1.893,25,1
+1,146,56,0,0,29.7,0.564,29,0
+2,71,70,27,0,28,0.586,22,0
+7,103,66,32,0,39.1,0.344,31,1
+7,105,0,0,0,0,0.305,24,0
+1,103,80,11,82,19.4,0.491,22,0
+1,101,50,15,36,24.2,0.526,26,0
+5,88,66,21,23,24.4,0.342,30,0
+8,176,90,34,300,33.7,0.467,58,1
+7,150,66,42,342,34.7,0.718,42,0
+1,73,50,10,0,23,0.248,21,0
+7,187,68,39,304,37.7,0.254,41,1
+0,100,88,60,110,46.8,0.962,31,0
+0,146,82,0,0,40.5,1.781,44,0
+0,105,64,41,142,41.5,0.173,22,0
+2,84,0,0,0,0,0.304,21,0
+8,133,72,0,0,32.9,0.27,39,1
+5,44,62,0,0,25,0.587,36,0
+2,141,58,34,128,25.4,0.699,24,0
+7,114,66,0,0,32.8,0.258,42,1
+5,99,74,27,0,29,0.203,32,0
+0,109,88,30,0,32.5,0.855,38,1
+2,109,92,0,0,42.7,0.845,54,0
+1,95,66,13,38,19.6,0.334,25,0
+4,146,85,27,100,28.9,0.189,27,0
+2,100,66,20,90,32.9,0.867,28,1
+5,139,64,35,140,28.6,0.411,26,0
+13,126,90,0,0,43.4,0.583,42,1
+4,129,86,20,270,35.1,0.231,23,0
+1,79,75,30,0,32,0.396,22,0
+1,0,48,20,0,24.7,0.14,22,0
+7,62,78,0,0,32.6,0.391,41,0
+5,95,72,33,0,37.7,0.37,27,0
+0,131,0,0,0,43.2,0.27,26,1
+2,112,66,22,0,25,0.307,24,0
+3,113,44,13,0,22.4,0.14,22,0
+2,74,0,0,0,0,0.102,22,0
+7,83,78,26,71,29.3,0.767,36,0
+0,101,65,28,0,24.6,0.237,22,0
+5,137,108,0,0,48.8,0.227,37,1
+2,110,74,29,125,32.4,0.698,27,0
+13,106,72,54,0,36.6,0.178,45,0
+2,100,68,25,71,38.5,0.324,26,0
+15,136,70,32,110,37.1,0.153,43,1
+1,107,68,19,0,26.5,0.165,24,0
+1,80,55,0,0,19.1,0.258,21,0
+4,123,80,15,176,32,0.443,34,0
+7,81,78,40,48,46.7,0.261,42,0
+4,134,72,0,0,23.8,0.277,60,1
+2,142,82,18,64,24.7,0.761,21,0
+6,144,72,27,228,33.9,0.255,40,0
+2,92,62,28,0,31.6,0.13,24,0
+1,71,48,18,76,20.4,0.323,22,0
+6,93,50,30,64,28.7,0.356,23,0
+1,122,90,51,220,49.7,0.325,31,1
+1,163,72,0,0,39,1.222,33,1
+1,151,60,0,0,26.1,0.179,22,0
+0,125,96,0,0,22.5,0.262,21,0
+1,81,72,18,40,26.6,0.283,24,0
+2,85,65,0,0,39.6,0.93,27,0
+1,126,56,29,152,28.7,0.801,21,0
+1,96,122,0,0,22.4,0.207,27,0
+4,144,58,28,140,29.5,0.287,37,0
+3,83,58,31,18,34.3,0.336,25,0
+0,95,85,25,36,37.4,0.247,24,1
+3,171,72,33,135,33.3,0.199,24,1
+8,155,62,26,495,34,0.543,46,1
+1,89,76,34,37,31.2,0.192,23,0
+4,76,62,0,0,34,0.391,25,0
+7,160,54,32,175,30.5,0.588,39,1
+4,146,92,0,0,31.2,0.539,61,1
+5,124,74,0,0,34,0.22,38,1
+5,78,48,0,0,33.7,0.654,25,0
+4,97,60,23,0,28.2,0.443,22,0
+4,99,76,15,51,23.2,0.223,21,0
+0,162,76,56,100,53.2,0.759,25,1
+6,111,64,39,0,34.2,0.26,24,0
+2,107,74,30,100,33.6,0.404,23,0
+5,132,80,0,0,26.8,0.186,69,0
+0,113,76,0,0,33.3,0.278,23,1
+1,88,30,42,99,55,0.496,26,1
+3,120,70,30,135,42.9,0.452,30,0
+1,118,58,36,94,33.3,0.261,23,0
+1,117,88,24,145,34.5,0.403,40,1
+0,105,84,0,0,27.9,0.741,62,1
+4,173,70,14,168,29.7,0.361,33,1
+9,122,56,0,0,33.3,1.114,33,1
+3,170,64,37,225,34.5,0.356,30,1
+8,84,74,31,0,38.3,0.457,39,0
+2,96,68,13,49,21.1,0.647,26,0
+2,125,60,20,140,33.8,0.088,31,0
+0,100,70,26,50,30.8,0.597,21,0
+0,93,60,25,92,28.7,0.532,22,0
+0,129,80,0,0,31.2,0.703,29,0
+5,105,72,29,325,36.9,0.159,28,0
+3,128,78,0,0,21.1,0.268,55,0
+5,106,82,30,0,39.5,0.286,38,0
+2,108,52,26,63,32.5,0.318,22,0
+10,108,66,0,0,32.4,0.272,42,1
+4,154,62,31,284,32.8,0.237,23,0
+0,102,75,23,0,0,0.572,21,0
+9,57,80,37,0,32.8,0.096,41,0
+2,106,64,35,119,30.5,1.4,34,0
+5,147,78,0,0,33.7,0.218,65,0
+2,90,70,17,0,27.3,0.085,22,0
+1,136,74,50,204,37.4,0.399,24,0
+4,114,65,0,0,21.9,0.432,37,0
+9,156,86,28,155,34.3,1.189,42,1
+1,153,82,42,485,40.6,0.687,23,0
+8,188,78,0,0,47.9,0.137,43,1
+7,152,88,44,0,50,0.337,36,1
+2,99,52,15,94,24.6,0.637,21,0
+1,109,56,21,135,25.2,0.833,23,0
+2,88,74,19,53,29,0.229,22,0
+17,163,72,41,114,40.9,0.817,47,1
+4,151,90,38,0,29.7,0.294,36,0
+7,102,74,40,105,37.2,0.204,45,0
+0,114,80,34,285,44.2,0.167,27,0
+2,100,64,23,0,29.7,0.368,21,0
+0,131,88,0,0,31.6,0.743,32,1
+6,104,74,18,156,29.9,0.722,41,1
+3,148,66,25,0,32.5,0.256,22,0
+4,120,68,0,0,29.6,0.709,34,0
+4,110,66,0,0,31.9,0.471,29,0
+3,111,90,12,78,28.4,0.495,29,0
+6,102,82,0,0,30.8,0.18,36,1
+6,134,70,23,130,35.4,0.542,29,1
+2,87,0,23,0,28.9,0.773,25,0
+1,79,60,42,48,43.5,0.678,23,0
+2,75,64,24,55,29.7,0.37,33,0
+8,179,72,42,130,32.7,0.719,36,1
+6,85,78,0,0,31.2,0.382,42,0
+0,129,110,46,130,67.1,0.319,26,1
+5,143,78,0,0,45,0.19,47,0
+5,130,82,0,0,39.1,0.956,37,1
+6,87,80,0,0,23.2,0.084,32,0
+0,119,64,18,92,34.9,0.725,23,0
+1,0,74,20,23,27.7,0.299,21,0
+5,73,60,0,0,26.8,0.268,27,0
+4,141,74,0,0,27.6,0.244,40,0
+7,194,68,28,0,35.9,0.745,41,1
+8,181,68,36,495,30.1,0.615,60,1
+1,128,98,41,58,32,1.321,33,1
+8,109,76,39,114,27.9,0.64,31,1
+5,139,80,35,160,31.6,0.361,25,1
+3,111,62,0,0,22.6,0.142,21,0
+9,123,70,44,94,33.1,0.374,40,0
+7,159,66,0,0,30.4,0.383,36,1
+11,135,0,0,0,52.3,0.578,40,1
+8,85,55,20,0,24.4,0.136,42,0
+5,158,84,41,210,39.4,0.395,29,1
+1,105,58,0,0,24.3,0.187,21,0
+3,107,62,13,48,22.9,0.678,23,1
+4,109,64,44,99,34.8,0.905,26,1
+4,148,60,27,318,30.9,0.15,29,1
+0,113,80,16,0,31,0.874,21,0
+1,138,82,0,0,40.1,0.236,28,0
+0,108,68,20,0,27.3,0.787,32,0
+2,99,70,16,44,20.4,0.235,27,0
+6,103,72,32,190,37.7,0.324,55,0
+5,111,72,28,0,23.9,0.407,27,0
+8,196,76,29,280,37.5,0.605,57,1
+5,162,104,0,0,37.7,0.151,52,1
+1,96,64,27,87,33.2,0.289,21,0
+7,184,84,33,0,35.5,0.355,41,1
+2,81,60,22,0,27.7,0.29,25,0
+0,147,85,54,0,42.8,0.375,24,0
+7,179,95,31,0,34.2,0.164,60,0
+0,140,65,26,130,42.6,0.431,24,1
+9,112,82,32,175,34.2,0.26,36,1
+12,151,70,40,271,41.8,0.742,38,1
+5,109,62,41,129,35.8,0.514,25,1
+6,125,68,30,120,30,0.464,32,0
+5,85,74,22,0,29,1.224,32,1
+5,112,66,0,0,37.8,0.261,41,1
+0,177,60,29,478,34.6,1.072,21,1
+2,158,90,0,0,31.6,0.805,66,1
+7,119,0,0,0,25.2,0.209,37,0
+7,142,60,33,190,28.8,0.687,61,0
+1,100,66,15,56,23.6,0.666,26,0
+1,87,78,27,32,34.6,0.101,22,0
+0,101,76,0,0,35.7,0.198,26,0
+3,162,52,38,0,37.2,0.652,24,1
+4,197,70,39,744,36.7,2.329,31,0
+0,117,80,31,53,45.2,0.089,24,0
+4,142,86,0,0,44,0.645,22,1
+6,134,80,37,370,46.2,0.238,46,1
+1,79,80,25,37,25.4,0.583,22,0
+4,122,68,0,0,35,0.394,29,0
+3,74,68,28,45,29.7,0.293,23,0
+4,171,72,0,0,43.6,0.479,26,1
+7,181,84,21,192,35.9,0.586,51,1
+0,179,90,27,0,44.1,0.686,23,1
+9,164,84,21,0,30.8,0.831,32,1
+0,104,76,0,0,18.4,0.582,27,0
+1,91,64,24,0,29.2,0.192,21,0
+4,91,70,32,88,33.1,0.446,22,0
+3,139,54,0,0,25.6,0.402,22,1
+6,119,50,22,176,27.1,1.318,33,1
+2,146,76,35,194,38.2,0.329,29,0
+9,184,85,15,0,30,1.213,49,1
+10,122,68,0,0,31.2,0.258,41,0
+0,165,90,33,680,52.3,0.427,23,0
+9,124,70,33,402,35.4,0.282,34,0
+1,111,86,19,0,30.1,0.143,23,0
+9,106,52,0,0,31.2,0.38,42,0
+2,129,84,0,0,28,0.284,27,0
+2,90,80,14,55,24.4,0.249,24,0
+0,86,68,32,0,35.8,0.238,25,0
+12,92,62,7,258,27.6,0.926,44,1
+1,113,64,35,0,33.6,0.543,21,1
+3,111,56,39,0,30.1,0.557,30,0
+2,114,68,22,0,28.7,0.092,25,0
+1,193,50,16,375,25.9,0.655,24,0
+11,155,76,28,150,33.3,1.353,51,1
+3,191,68,15,130,30.9,0.299,34,0
+3,141,0,0,0,30,0.761,27,1
+4,95,70,32,0,32.1,0.612,24,0
+3,142,80,15,0,32.4,0.2,63,0
+4,123,62,0,0,32,0.226,35,1
+5,96,74,18,67,33.6,0.997,43,0
+0,138,0,0,0,36.3,0.933,25,1
+2,128,64,42,0,40,1.101,24,0
+0,102,52,0,0,25.1,0.078,21,0
+2,146,0,0,0,27.5,0.24,28,1
+10,101,86,37,0,45.6,1.136,38,1
+2,108,62,32,56,25.2,0.128,21,0
+3,122,78,0,0,23,0.254,40,0
+1,71,78,50,45,33.2,0.422,21,0
+13,106,70,0,0,34.2,0.251,52,0
+2,100,70,52,57,40.5,0.677,25,0
+7,106,60,24,0,26.5,0.296,29,1
+0,104,64,23,116,27.8,0.454,23,0
+5,114,74,0,0,24.9,0.744,57,0
+2,108,62,10,278,25.3,0.881,22,0
+0,146,70,0,0,37.9,0.334,28,1
+10,129,76,28,122,35.9,0.28,39,0
+7,133,88,15,155,32.4,0.262,37,0
+7,161,86,0,0,30.4,0.165,47,1
+2,108,80,0,0,27,0.259,52,1
+7,136,74,26,135,26,0.647,51,0
+5,155,84,44,545,38.7,0.619,34,0
+1,119,86,39,220,45.6,0.808,29,1
+4,96,56,17,49,20.8,0.34,26,0
+5,108,72,43,75,36.1,0.263,33,0
+0,78,88,29,40,36.9,0.434,21,0
+0,107,62,30,74,36.6,0.757,25,1
+2,128,78,37,182,43.3,1.224,31,1
+1,128,48,45,194,40.5,0.613,24,1
+0,161,50,0,0,21.9,0.254,65,0
+6,151,62,31,120,35.5,0.692,28,0
+2,146,70,38,360,28,0.337,29,1
+0,126,84,29,215,30.7,0.52,24,0
+14,100,78,25,184,36.6,0.412,46,1
+8,112,72,0,0,23.6,0.84,58,0
+0,167,0,0,0,32.3,0.839,30,1
+2,144,58,33,135,31.6,0.422,25,1
+5,77,82,41,42,35.8,0.156,35,0
+5,115,98,0,0,52.9,0.209,28,1
+3,150,76,0,0,21,0.207,37,0
+2,120,76,37,105,39.7,0.215,29,0
+10,161,68,23,132,25.5,0.326,47,1
+0,137,68,14,148,24.8,0.143,21,0
+0,128,68,19,180,30.5,1.391,25,1
+2,124,68,28,205,32.9,0.875,30,1
+6,80,66,30,0,26.2,0.313,41,0
+0,106,70,37,148,39.4,0.605,22,0
+2,155,74,17,96,26.6,0.433,27,1
+3,113,50,10,85,29.5,0.626,25,0
+7,109,80,31,0,35.9,1.127,43,1
+2,112,68,22,94,34.1,0.315,26,0
+3,99,80,11,64,19.3,0.284,30,0
+3,182,74,0,0,30.5,0.345,29,1
+3,115,66,39,140,38.1,0.15,28,0
+6,194,78,0,0,23.5,0.129,59,1
+4,129,60,12,231,27.5,0.527,31,0
+3,112,74,30,0,31.6,0.197,25,1
+0,124,70,20,0,27.4,0.254,36,1
+13,152,90,33,29,26.8,0.731,43,1
+2,112,75,32,0,35.7,0.148,21,0
+1,157,72,21,168,25.6,0.123,24,0
+1,122,64,32,156,35.1,0.692,30,1
+10,179,70,0,0,35.1,0.2,37,0
+2,102,86,36,120,45.5,0.127,23,1
+6,105,70,32,68,30.8,0.122,37,0
+8,118,72,19,0,23.1,1.476,46,0
+2,87,58,16,52,32.7,0.166,25,0
+1,180,0,0,0,43.3,0.282,41,1
+12,106,80,0,0,23.6,0.137,44,0
+1,95,60,18,58,23.9,0.26,22,0
+0,165,76,43,255,47.9,0.259,26,0
+0,117,0,0,0,33.8,0.932,44,0
+5,115,76,0,0,31.2,0.343,44,1
+9,152,78,34,171,34.2,0.893,33,1
+7,178,84,0,0,39.9,0.331,41,1
+1,130,70,13,105,25.9,0.472,22,0
+1,95,74,21,73,25.9,0.673,36,0
+1,0,68,35,0,32,0.389,22,0
+5,122,86,0,0,34.7,0.29,33,0
+8,95,72,0,0,36.8,0.485,57,0
+8,126,88,36,108,38.5,0.349,49,0
+1,139,46,19,83,28.7,0.654,22,0
+3,116,0,0,0,23.5,0.187,23,0
+3,99,62,19,74,21.8,0.279,26,0
+5,0,80,32,0,41,0.346,37,1
+4,92,80,0,0,42.2,0.237,29,0
+4,137,84,0,0,31.2,0.252,30,0
+3,61,82,28,0,34.4,0.243,46,0
+1,90,62,12,43,27.2,0.58,24,0
+3,90,78,0,0,42.7,0.559,21,0
+9,165,88,0,0,30.4,0.302,49,1
+1,125,50,40,167,33.3,0.962,28,1
+13,129,0,30,0,39.9,0.569,44,1
+12,88,74,40,54,35.3,0.378,48,0
+1,196,76,36,249,36.5,0.875,29,1
+5,189,64,33,325,31.2,0.583,29,1
+5,158,70,0,0,29.8,0.207,63,0
+5,103,108,37,0,39.2,0.305,65,0
+4,146,78,0,0,38.5,0.52,67,1
+4,147,74,25,293,34.9,0.385,30,0
+5,99,54,28,83,34,0.499,30,0
+6,124,72,0,0,27.6,0.368,29,1
+0,101,64,17,0,21,0.252,21,0
+3,81,86,16,66,27.5,0.306,22,0
+1,133,102,28,140,32.8,0.234,45,1
+3,173,82,48,465,38.4,2.137,25,1
+0,118,64,23,89,0,1.731,21,0
+0,84,64,22,66,35.8,0.545,21,0
+2,105,58,40,94,34.9,0.225,25,0
+2,122,52,43,158,36.2,0.816,28,0
+12,140,82,43,325,39.2,0.528,58,1
+0,98,82,15,84,25.2,0.299,22,0
+1,87,60,37,75,37.2,0.509,22,0
+4,156,75,0,0,48.3,0.238,32,1
+0,93,100,39,72,43.4,1.021,35,0
+1,107,72,30,82,30.8,0.821,24,0
+0,105,68,22,0,20,0.236,22,0
+1,109,60,8,182,25.4,0.947,21,0
+1,90,62,18,59,25.1,1.268,25,0
+1,125,70,24,110,24.3,0.221,25,0
+1,119,54,13,50,22.3,0.205,24,0
+5,116,74,29,0,32.3,0.66,35,1
+8,105,100,36,0,43.3,0.239,45,1
+5,144,82,26,285,32,0.452,58,1
+3,100,68,23,81,31.6,0.949,28,0
+1,100,66,29,196,32,0.444,42,0
+5,166,76,0,0,45.7,0.34,27,1
+1,131,64,14,415,23.7,0.389,21,0
+4,116,72,12,87,22.1,0.463,37,0
+4,158,78,0,0,32.9,0.803,31,1
+2,127,58,24,275,27.7,1.6,25,0
+3,96,56,34,115,24.7,0.944,39,0
+0,131,66,40,0,34.3,0.196,22,1
+3,82,70,0,0,21.1,0.389,25,0
+3,193,70,31,0,34.9,0.241,25,1
+4,95,64,0,0,32,0.161,31,1
+6,137,61,0,0,24.2,0.151,55,0
+5,136,84,41,88,35,0.286,35,1
+9,72,78,25,0,31.6,0.28,38,0
+5,168,64,0,0,32.9,0.135,41,1
+2,123,48,32,165,42.1,0.52,26,0
+4,115,72,0,0,28.9,0.376,46,1
+0,101,62,0,0,21.9,0.336,25,0
+8,197,74,0,0,25.9,1.191,39,1
+1,172,68,49,579,42.4,0.702,28,1
+6,102,90,39,0,35.7,0.674,28,0
+1,112,72,30,176,34.4,0.528,25,0
+1,143,84,23,310,42.4,1.076,22,0
+1,143,74,22,61,26.2,0.256,21,0
+0,138,60,35,167,34.6,0.534,21,1
+3,173,84,33,474,35.7,0.258,22,1
+1,97,68,21,0,27.2,1.095,22,0
+4,144,82,32,0,38.5,0.554,37,1
+1,83,68,0,0,18.2,0.624,27,0
+3,129,64,29,115,26.4,0.219,28,1
+1,119,88,41,170,45.3,0.507,26,0
+2,94,68,18,76,26,0.561,21,0
+0,102,64,46,78,40.6,0.496,21,0
+2,115,64,22,0,30.8,0.421,21,0
+8,151,78,32,210,42.9,0.516,36,1
+4,184,78,39,277,37,0.264,31,1
+0,94,0,0,0,0,0.256,25,0
+1,181,64,30,180,34.1,0.328,38,1
+0,135,94,46,145,40.6,0.284,26,0
+1,95,82,25,180,35,0.233,43,1
+2,99,0,0,0,22.2,0.108,23,0
+3,89,74,16,85,30.4,0.551,38,0
+1,80,74,11,60,30,0.527,22,0
+2,139,75,0,0,25.6,0.167,29,0
+1,90,68,8,0,24.5,1.138,36,0
+0,141,0,0,0,42.4,0.205,29,1
+12,140,85,33,0,37.4,0.244,41,0
+5,147,75,0,0,29.9,0.434,28,0
+1,97,70,15,0,18.2,0.147,21,0
+6,107,88,0,0,36.8,0.727,31,0
+0,189,104,25,0,34.3,0.435,41,1
+2,83,66,23,50,32.2,0.497,22,0
+4,117,64,27,120,33.2,0.23,24,0
+8,108,70,0,0,30.5,0.955,33,1
+4,117,62,12,0,29.7,0.38,30,1
+0,180,78,63,14,59.4,2.42,25,1
+1,100,72,12,70,25.3,0.658,28,0
+0,95,80,45,92,36.5,0.33,26,0
+0,104,64,37,64,33.6,0.51,22,1
+0,120,74,18,63,30.5,0.285,26,0
+1,82,64,13,95,21.2,0.415,23,0
+2,134,70,0,0,28.9,0.542,23,1
+0,91,68,32,210,39.9,0.381,25,0
+2,119,0,0,0,19.6,0.832,72,0
+2,100,54,28,105,37.8,0.498,24,0
+14,175,62,30,0,33.6,0.212,38,1
+1,135,54,0,0,26.7,0.687,62,0
+5,86,68,28,71,30.2,0.364,24,0
+10,148,84,48,237,37.6,1.001,51,1
+9,134,74,33,60,25.9,0.46,81,0
+9,120,72,22,56,20.8,0.733,48,0
+1,71,62,0,0,21.8,0.416,26,0
+8,74,70,40,49,35.3,0.705,39,0
+5,88,78,30,0,27.6,0.258,37,0
+10,115,98,0,0,24,1.022,34,0
+0,124,56,13,105,21.8,0.452,21,0
+0,74,52,10,36,27.8,0.269,22,0
+0,97,64,36,100,36.8,0.6,25,0
+8,120,0,0,0,30,0.183,38,1
+6,154,78,41,140,46.1,0.571,27,0
+1,144,82,40,0,41.3,0.607,28,0
+0,137,70,38,0,33.2,0.17,22,0
+0,119,66,27,0,38.8,0.259,22,0
+7,136,90,0,0,29.9,0.21,50,0
+4,114,64,0,0,28.9,0.126,24,0
+0,137,84,27,0,27.3,0.231,59,0
+2,105,80,45,191,33.7,0.711,29,1
+7,114,76,17,110,23.8,0.466,31,0
+8,126,74,38,75,25.9,0.162,39,0
+4,132,86,31,0,28,0.419,63,0
+3,158,70,30,328,35.5,0.344,35,1
+0,123,88,37,0,35.2,0.197,29,0
+4,85,58,22,49,27.8,0.306,28,0
+0,84,82,31,125,38.2,0.233,23,0
+0,145,0,0,0,44.2,0.63,31,1
+0,135,68,42,250,42.3,0.365,24,1
+1,139,62,41,480,40.7,0.536,21,0
+0,173,78,32,265,46.5,1.159,58,0
+4,99,72,17,0,25.6,0.294,28,0
+8,194,80,0,0,26.1,0.551,67,0
+2,83,65,28,66,36.8,0.629,24,0
+2,89,90,30,0,33.5,0.292,42,0
+4,99,68,38,0,32.8,0.145,33,0
+4,125,70,18,122,28.9,1.144,45,1
+3,80,0,0,0,0,0.174,22,0
+6,166,74,0,0,26.6,0.304,66,0
+5,110,68,0,0,26,0.292,30,0
+2,81,72,15,76,30.1,0.547,25,0
+7,195,70,33,145,25.1,0.163,55,1
+6,154,74,32,193,29.3,0.839,39,0
+2,117,90,19,71,25.2,0.313,21,0
+3,84,72,32,0,37.2,0.267,28,0
+6,0,68,41,0,39,0.727,41,1
+7,94,64,25,79,33.3,0.738,41,0
+3,96,78,39,0,37.3,0.238,40,0
+10,75,82,0,0,33.3,0.263,38,0
+0,180,90,26,90,36.5,0.314,35,1
+1,130,60,23,170,28.6,0.692,21,0
+2,84,50,23,76,30.4,0.968,21,0
+8,120,78,0,0,25,0.409,64,0
+12,84,72,31,0,29.7,0.297,46,1
+0,139,62,17,210,22.1,0.207,21,0
+9,91,68,0,0,24.2,0.2,58,0
+2,91,62,0,0,27.3,0.525,22,0
+3,99,54,19,86,25.6,0.154,24,0
+3,163,70,18,105,31.6,0.268,28,1
+9,145,88,34,165,30.3,0.771,53,1
+7,125,86,0,0,37.6,0.304,51,0
+13,76,60,0,0,32.8,0.18,41,0
+6,129,90,7,326,19.6,0.582,60,0
+2,68,70,32,66,25,0.187,25,0
+3,124,80,33,130,33.2,0.305,26,0
+6,114,0,0,0,0,0.189,26,0
+9,130,70,0,0,34.2,0.652,45,1
+3,125,58,0,0,31.6,0.151,24,0
+3,87,60,18,0,21.8,0.444,21,0
+1,97,64,19,82,18.2,0.299,21,0
+3,116,74,15,105,26.3,0.107,24,0
+0,117,66,31,188,30.8,0.493,22,0
+0,111,65,0,0,24.6,0.66,31,0
+2,122,60,18,106,29.8,0.717,22,0
+0,107,76,0,0,45.3,0.686,24,0
+1,86,66,52,65,41.3,0.917,29,0
+6,91,0,0,0,29.8,0.501,31,0
+1,77,56,30,56,33.3,1.251,24,0
+4,132,0,0,0,32.9,0.302,23,1
+0,105,90,0,0,29.6,0.197,46,0
+0,57,60,0,0,21.7,0.735,67,0
+0,127,80,37,210,36.3,0.804,23,0
+3,129,92,49,155,36.4,0.968,32,1
+8,100,74,40,215,39.4,0.661,43,1
+3,128,72,25,190,32.4,0.549,27,1
+10,90,85,32,0,34.9,0.825,56,1
+4,84,90,23,56,39.5,0.159,25,0
+1,88,78,29,76,32,0.365,29,0
+8,186,90,35,225,34.5,0.423,37,1
+5,187,76,27,207,43.6,1.034,53,1
+4,131,68,21,166,33.1,0.16,28,0
+1,164,82,43,67,32.8,0.341,50,0
+4,189,110,31,0,28.5,0.68,37,0
+1,116,70,28,0,27.4,0.204,21,0
+3,84,68,30,106,31.9,0.591,25,0
+6,114,88,0,0,27.8,0.247,66,0
+1,88,62,24,44,29.9,0.422,23,0
+1,84,64,23,115,36.9,0.471,28,0
+7,124,70,33,215,25.5,0.161,37,0
+1,97,70,40,0,38.1,0.218,30,0
+8,110,76,0,0,27.8,0.237,58,0
+11,103,68,40,0,46.2,0.126,42,0
+11,85,74,0,0,30.1,0.3,35,0
+6,125,76,0,0,33.8,0.121,54,1
+0,198,66,32,274,41.3,0.502,28,1
+1,87,68,34,77,37.6,0.401,24,0
+6,99,60,19,54,26.9,0.497,32,0
+0,91,80,0,0,32.4,0.601,27,0
+2,95,54,14,88,26.1,0.748,22,0
+1,99,72,30,18,38.6,0.412,21,0
+6,92,62,32,126,32,0.085,46,0
+4,154,72,29,126,31.3,0.338,37,0
+0,121,66,30,165,34.3,0.203,33,1
+3,78,70,0,0,32.5,0.27,39,0
+2,130,96,0,0,22.6,0.268,21,0
+3,111,58,31,44,29.5,0.43,22,0
+2,98,60,17,120,34.7,0.198,22,0
+1,143,86,30,330,30.1,0.892,23,0
+1,119,44,47,63,35.5,0.28,25,0
+6,108,44,20,130,24,0.813,35,0
+2,118,80,0,0,42.9,0.693,21,1
+10,133,68,0,0,27,0.245,36,0
+2,197,70,99,0,34.7,0.575,62,1
+0,151,90,46,0,42.1,0.371,21,1
+6,109,60,27,0,25,0.206,27,0
+12,121,78,17,0,26.5,0.259,62,0
+8,100,76,0,0,38.7,0.19,42,0
+8,124,76,24,600,28.7,0.687,52,1
+1,93,56,11,0,22.5,0.417,22,0
+8,143,66,0,0,34.9,0.129,41,1
+6,103,66,0,0,24.3,0.249,29,0
+3,176,86,27,156,33.3,1.154,52,1
+0,73,0,0,0,21.1,0.342,25,0
+11,111,84,40,0,46.8,0.925,45,1
+2,112,78,50,140,39.4,0.175,24,0
+3,132,80,0,0,34.4,0.402,44,1
+2,82,52,22,115,28.5,1.699,25,0
+6,123,72,45,230,33.6,0.733,34,0
+0,188,82,14,185,32,0.682,22,1
+0,67,76,0,0,45.3,0.194,46,0
+1,89,24,19,25,27.8,0.559,21,0
+1,173,74,0,0,36.8,0.088,38,1
+1,109,38,18,120,23.1,0.407,26,0
+1,108,88,19,0,27.1,0.4,24,0
+6,96,0,0,0,23.7,0.19,28,0
+1,124,74,36,0,27.8,0.1,30,0
+7,150,78,29,126,35.2,0.692,54,1
+4,183,0,0,0,28.4,0.212,36,1
+1,124,60,32,0,35.8,0.514,21,0
+1,181,78,42,293,40,1.258,22,1
+1,92,62,25,41,19.5,0.482,25,0
+0,152,82,39,272,41.5,0.27,27,0
+1,111,62,13,182,24,0.138,23,0
+3,106,54,21,158,30.9,0.292,24,0
+3,174,58,22,194,32.9,0.593,36,1
+7,168,88,42,321,38.2,0.787,40,1
+6,105,80,28,0,32.5,0.878,26,0
+11,138,74,26,144,36.1,0.557,50,1
+3,106,72,0,0,25.8,0.207,27,0
+6,117,96,0,0,28.7,0.157,30,0
+2,68,62,13,15,20.1,0.257,23,0
+9,112,82,24,0,28.2,1.282,50,1
+0,119,0,0,0,32.4,0.141,24,1
+2,112,86,42,160,38.4,0.246,28,0
+2,92,76,20,0,24.2,1.698,28,0
+6,183,94,0,0,40.8,1.461,45,0
+0,94,70,27,115,43.5,0.347,21,0
+2,108,64,0,0,30.8,0.158,21,0
+4,90,88,47,54,37.7,0.362,29,0
+0,125,68,0,0,24.7,0.206,21,0
+0,132,78,0,0,32.4,0.393,21,0
+5,128,80,0,0,34.6,0.144,45,0
+4,94,65,22,0,24.7,0.148,21,0
+7,114,64,0,0,27.4,0.732,34,1
+0,102,78,40,90,34.5,0.238,24,0
+2,111,60,0,0,26.2,0.343,23,0
+1,128,82,17,183,27.5,0.115,22,0
+10,92,62,0,0,25.9,0.167,31,0
+13,104,72,0,0,31.2,0.465,38,1
+5,104,74,0,0,28.8,0.153,48,0
+2,94,76,18,66,31.6,0.649,23,0
+7,97,76,32,91,40.9,0.871,32,1
+1,100,74,12,46,19.5,0.149,28,0
+0,102,86,17,105,29.3,0.695,27,0
+4,128,70,0,0,34.3,0.303,24,0
+6,147,80,0,0,29.5,0.178,50,1
+4,90,0,0,0,28,0.61,31,0
+3,103,72,30,152,27.6,0.73,27,0
+2,157,74,35,440,39.4,0.134,30,0
+1,167,74,17,144,23.4,0.447,33,1
+0,179,50,36,159,37.8,0.455,22,1
+11,136,84,35,130,28.3,0.26,42,1
+0,107,60,25,0,26.4,0.133,23,0
+1,91,54,25,100,25.2,0.234,23,0
+1,117,60,23,106,33.8,0.466,27,0
+5,123,74,40,77,34.1,0.269,28,0
+2,120,54,0,0,26.8,0.455,27,0
+1,106,70,28,135,34.2,0.142,22,0
+2,155,52,27,540,38.7,0.24,25,1
+2,101,58,35,90,21.8,0.155,22,0
+1,120,80,48,200,38.9,1.162,41,0
+11,127,106,0,0,39,0.19,51,0
+3,80,82,31,70,34.2,1.292,27,1
+10,162,84,0,0,27.7,0.182,54,0
+1,199,76,43,0,42.9,1.394,22,1
+8,167,106,46,231,37.6,0.165,43,1
+9,145,80,46,130,37.9,0.637,40,1
+6,115,60,39,0,33.7,0.245,40,1
+1,112,80,45,132,34.8,0.217,24,0
+4,145,82,18,0,32.5,0.235,70,1
+10,111,70,27,0,27.5,0.141,40,1
+6,98,58,33,190,34,0.43,43,0
+9,154,78,30,100,30.9,0.164,45,0
+6,165,68,26,168,33.6,0.631,49,0
+1,99,58,10,0,25.4,0.551,21,0
+10,68,106,23,49,35.5,0.285,47,0
+3,123,100,35,240,57.3,0.88,22,0
+8,91,82,0,0,35.6,0.587,68,0
+6,195,70,0,0,30.9,0.328,31,1
+9,156,86,0,0,24.8,0.23,53,1
+0,93,60,0,0,35.3,0.263,25,0
+3,121,52,0,0,36,0.127,25,1
+2,101,58,17,265,24.2,0.614,23,0
+2,56,56,28,45,24.2,0.332,22,0
+0,162,76,36,0,49.6,0.364,26,1
+0,95,64,39,105,44.6,0.366,22,0
+4,125,80,0,0,32.3,0.536,27,1
+5,136,82,0,0,0,0.64,69,0
+2,129,74,26,205,33.2,0.591,25,0
+3,130,64,0,0,23.1,0.314,22,0
+1,107,50,19,0,28.3,0.181,29,0
+1,140,74,26,180,24.1,0.828,23,0
+1,144,82,46,180,46.1,0.335,46,1
+8,107,80,0,0,24.6,0.856,34,0
+13,158,114,0,0,42.3,0.257,44,1
+2,121,70,32,95,39.1,0.886,23,0
+7,129,68,49,125,38.5,0.439,43,1
+2,90,60,0,0,23.5,0.191,25,0
+7,142,90,24,480,30.4,0.128,43,1
+3,169,74,19,125,29.9,0.268,31,1
+0,99,0,0,0,25,0.253,22,0
+4,127,88,11,155,34.5,0.598,28,0
+4,118,70,0,0,44.5,0.904,26,0
+2,122,76,27,200,35.9,0.483,26,0
+6,125,78,31,0,27.6,0.565,49,1
+1,168,88,29,0,35,0.905,52,1
+2,129,0,0,0,38.5,0.304,41,0
+4,110,76,20,100,28.4,0.118,27,0
+6,80,80,36,0,39.8,0.177,28,0
+10,115,0,0,0,0,0.261,30,1
+2,127,46,21,335,34.4,0.176,22,0
+9,164,78,0,0,32.8,0.148,45,1
+2,93,64,32,160,38,0.674,23,1
+3,158,64,13,387,31.2,0.295,24,0
+5,126,78,27,22,29.6,0.439,40,0
+10,129,62,36,0,41.2,0.441,38,1
+0,134,58,20,291,26.4,0.352,21,0
+3,102,74,0,0,29.5,0.121,32,0
+7,187,50,33,392,33.9,0.826,34,1
+3,173,78,39,185,33.8,0.97,31,1
+10,94,72,18,0,23.1,0.595,56,0
+1,108,60,46,178,35.5,0.415,24,0
+5,97,76,27,0,35.6,0.378,52,1
+4,83,86,19,0,29.3,0.317,34,0
+1,114,66,36,200,38.1,0.289,21,0
+1,149,68,29,127,29.3,0.349,42,1
+5,117,86,30,105,39.1,0.251,42,0
+1,111,94,0,0,32.8,0.265,45,0
+4,112,78,40,0,39.4,0.236,38,0
+1,116,78,29,180,36.1,0.496,25,0
+0,141,84,26,0,32.4,0.433,22,0
+2,175,88,0,0,22.9,0.326,22,0
+2,92,52,0,0,30.1,0.141,22,0
+3,130,78,23,79,28.4,0.323,34,1
+8,120,86,0,0,28.4,0.259,22,1
+2,174,88,37,120,44.5,0.646,24,1
+2,106,56,27,165,29,0.426,22,0
+2,105,75,0,0,23.3,0.56,53,0
+4,95,60,32,0,35.4,0.284,28,0
+0,126,86,27,120,27.4,0.515,21,0
+8,65,72,23,0,32,0.6,42,0
+2,99,60,17,160,36.6,0.453,21,0
+1,102,74,0,0,39.5,0.293,42,1
+11,120,80,37,150,42.3,0.785,48,1
+3,102,44,20,94,30.8,0.4,26,0
+1,109,58,18,116,28.5,0.219,22,0
+9,140,94,0,0,32.7,0.734,45,1
+13,153,88,37,140,40.6,1.174,39,0
+12,100,84,33,105,30,0.488,46,0
+1,147,94,41,0,49.3,0.358,27,1
+1,81,74,41,57,46.3,1.096,32,0
+3,187,70,22,200,36.4,0.408,36,1
+6,162,62,0,0,24.3,0.178,50,1
+4,136,70,0,0,31.2,1.182,22,1
+1,121,78,39,74,39,0.261,28,0
+3,108,62,24,0,26,0.223,25,0
+0,181,88,44,510,43.3,0.222,26,1
+8,154,78,32,0,32.4,0.443,45,1
+1,128,88,39,110,36.5,1.057,37,1
+7,137,90,41,0,32,0.391,39,0
+0,123,72,0,0,36.3,0.258,52,1
+1,106,76,0,0,37.5,0.197,26,0
+6,190,92,0,0,35.5,0.278,66,1
+2,88,58,26,16,28.4,0.766,22,0
+9,170,74,31,0,44,0.403,43,1
+9,89,62,0,0,22.5,0.142,33,0
+10,101,76,48,180,32.9,0.171,63,0
+2,122,70,27,0,36.8,0.34,27,0
+5,121,72,23,112,26.2,0.245,30,0
+1,126,60,0,0,30.1,0.349,47,1
+1,93,70,31,0,30.4,0.315,23,0

diabetes/diabetes.csv

@@ -0,0 +1,769 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
+1,97,66,15,140,23.2,0.487,22,0
+13,145,82,19,110,22.2,0.245,57,0
+5,117,92,0,0,34.1,0.337,38,0
+5,109,75,26,0,36,0.546,60,0
+3,158,76,36,245,31.6,0.851,28,1
+3,88,58,11,54,24.8,0.267,22,0
+6,92,92,0,0,19.9,0.188,28,0
+10,122,78,31,0,27.6,0.512,45,0
+4,103,60,33,192,24,0.966,33,0
+11,138,76,0,0,33.2,0.42,35,0
+9,102,76,37,0,32.9,0.665,46,1
+2,90,68,42,0,38.2,0.503,27,1
+4,111,72,47,207,37.1,1.39,56,1
+3,180,64,25,70,34,0.271,26,0
+7,133,84,0,0,40.2,0.696,37,0
+7,106,92,18,0,22.7,0.235,48,0
+9,171,110,24,240,45.4,0.721,54,1
+7,159,64,0,0,27.4,0.294,40,0
+0,180,66,39,0,42,1.893,25,1
+1,146,56,0,0,29.7,0.564,29,0
+2,71,70,27,0,28,0.586,22,0
+7,103,66,32,0,39.1,0.344,31,1
+7,105,0,0,0,0,0.305,24,0
+1,103,80,11,82,19.4,0.491,22,0
+1,101,50,15,36,24.2,0.526,26,0
+5,88,66,21,23,24.4,0.342,30,0
+8,176,90,34,300,33.7,0.467,58,1
+7,150,66,42,342,34.7,0.718,42,0
+1,73,50,10,0,23,0.248,21,0
+7,187,68,39,304,37.7,0.254,41,1
+0,100,88,60,110,46.8,0.962,31,0
+0,146,82,0,0,40.5,1.781,44,0
+0,105,64,41,142,41.5,0.173,22,0
+2,84,0,0,0,0,0.304,21,0
+8,133,72,0,0,32.9,0.27,39,1
+5,44,62,0,0,25,0.587,36,0
+2,141,58,34,128,25.4,0.699,24,0
+7,114,66,0,0,32.8,0.258,42,1
+5,99,74,27,0,29,0.203,32,0
+0,109,88,30,0,32.5,0.855,38,1
+2,109,92,0,0,42.7,0.845,54,0
+1,95,66,13,38,19.6,0.334,25,0
+4,146,85,27,100,28.9,0.189,27,0
+2,100,66,20,90,32.9,0.867,28,1
+5,139,64,35,140,28.6,0.411,26,0
+13,126,90,0,0,43.4,0.583,42,1
+4,129,86,20,270,35.1,0.231,23,0
+1,79,75,30,0,32,0.396,22,0
+1,0,48,20,0,24.7,0.14,22,0
+7,62,78,0,0,32.6,0.391,41,0
+5,95,72,33,0,37.7,0.37,27,0
+0,131,0,0,0,43.2,0.27,26,1
+2,112,66,22,0,25,0.307,24,0
+3,113,44,13,0,22.4,0.14,22,0
+2,74,0,0,0,0,0.102,22,0
+7,83,78,26,71,29.3,0.767,36,0
+0,101,65,28,0,24.6,0.237,22,0
+5,137,108,0,0,48.8,0.227,37,1
+2,110,74,29,125,32.4,0.698,27,0
+13,106,72,54,0,36.6,0.178,45,0
+2,100,68,25,71,38.5,0.324,26,0
+15,136,70,32,110,37.1,0.153,43,1
+1,107,68,19,0,26.5,0.165,24,0
+1,80,55,0,0,19.1,0.258,21,0
+4,123,80,15,176,32,0.443,34,0
+7,81,78,40,48,46.7,0.261,42,0
+4,134,72,0,0,23.8,0.277,60,1
+2,142,82,18,64,24.7,0.761,21,0
+6,144,72,27,228,33.9,0.255,40,0
+2,92,62,28,0,31.6,0.13,24,0
+1,71,48,18,76,20.4,0.323,22,0
+6,93,50,30,64,28.7,0.356,23,0
+1,122,90,51,220,49.7,0.325,31,1
+1,163,72,0,0,39,1.222,33,1
+1,151,60,0,0,26.1,0.179,22,0
+0,125,96,0,0,22.5,0.262,21,0
+1,81,72,18,40,26.6,0.283,24,0
+2,85,65,0,0,39.6,0.93,27,0
+1,126,56,29,152,28.7,0.801,21,0
+1,96,122,0,0,22.4,0.207,27,0
+4,144,58,28,140,29.5,0.287,37,0
+3,83,58,31,18,34.3,0.336,25,0
+0,95,85,25,36,37.4,0.247,24,1
+3,171,72,33,135,33.3,0.199,24,1
+8,155,62,26,495,34,0.543,46,1
+1,89,76,34,37,31.2,0.192,23,0
+4,76,62,0,0,34,0.391,25,0
+7,160,54,32,175,30.5,0.588,39,1
+4,146,92,0,0,31.2,0.539,61,1
+5,124,74,0,0,34,0.22,38,1
+5,78,48,0,0,33.7,0.654,25,0
+4,97,60,23,0,28.2,0.443,22,0
+4,99,76,15,51,23.2,0.223,21,0
+0,162,76,56,100,53.2,0.759,25,1
+6,111,64,39,0,34.2,0.26,24,0
+2,107,74,30,100,33.6,0.404,23,0
+5,132,80,0,0,26.8,0.186,69,0
+0,113,76,0,0,33.3,0.278,23,1
+1,88,30,42,99,55,0.496,26,1
+3,120,70,30,135,42.9,0.452,30,0
+1,118,58,36,94,33.3,0.261,23,0
+1,117,88,24,145,34.5,0.403,40,1
+0,105,84,0,0,27.9,0.741,62,1
+4,173,70,14,168,29.7,0.361,33,1
+9,122,56,0,0,33.3,1.114,33,1
+3,170,64,37,225,34.5,0.356,30,1
+8,84,74,31,0,38.3,0.457,39,0
+2,96,68,13,49,21.1,0.647,26,0
+2,125,60,20,140,33.8,0.088,31,0
+0,100,70,26,50,30.8,0.597,21,0
+0,93,60,25,92,28.7,0.532,22,0
+0,129,80,0,0,31.2,0.703,29,0
+5,105,72,29,325,36.9,0.159,28,0
+3,128,78,0,0,21.1,0.268,55,0
+5,106,82,30,0,39.5,0.286,38,0
+2,108,52,26,63,32.5,0.318,22,0
+10,108,66,0,0,32.4,0.272,42,1
+4,154,62,31,284,32.8,0.237,23,0
+0,102,75,23,0,0,0.572,21,0
+9,57,80,37,0,32.8,0.096,41,0
+2,106,64,35,119,30.5,1.4,34,0
+5,147,78,0,0,33.7,0.218,65,0
+2,90,70,17,0,27.3,0.085,22,0
+1,136,74,50,204,37.4,0.399,24,0
+4,114,65,0,0,21.9,0.432,37,0
+9,156,86,28,155,34.3,1.189,42,1
+1,153,82,42,485,40.6,0.687,23,0
+8,188,78,0,0,47.9,0.137,43,1
+7,152,88,44,0,50,0.337,36,1
+2,99,52,15,94,24.6,0.637,21,0
+1,109,56,21,135,25.2,0.833,23,0
+2,88,74,19,53,29,0.229,22,0
+17,163,72,41,114,40.9,0.817,47,1
+4,151,90,38,0,29.7,0.294,36,0
+7,102,74,40,105,37.2,0.204,45,0
+0,114,80,34,285,44.2,0.167,27,0
+2,100,64,23,0,29.7,0.368,21,0
+0,131,88,0,0,31.6,0.743,32,1
+6,104,74,18,156,29.9,0.722,41,1
+3,148,66,25,0,32.5,0.256,22,0
+4,120,68,0,0,29.6,0.709,34,0
+4,110,66,0,0,31.9,0.471,29,0
+3,111,90,12,78,28.4,0.495,29,0
+6,102,82,0,0,30.8,0.18,36,1
+6,134,70,23,130,35.4,0.542,29,1
+2,87,0,23,0,28.9,0.773,25,0
+1,79,60,42,48,43.5,0.678,23,0
+2,75,64,24,55,29.7,0.37,33,0
+8,179,72,42,130,32.7,0.719,36,1
+6,85,78,0,0,31.2,0.382,42,0
+0,129,110,46,130,67.1,0.319,26,1
+5,143,78,0,0,45,0.19,47,0
+5,130,82,0,0,39.1,0.956,37,1
+6,87,80,0,0,23.2,0.084,32,0
+0,119,64,18,92,34.9,0.725,23,0
+1,0,74,20,23,27.7,0.299,21,0
+5,73,60,0,0,26.8,0.268,27,0
+4,141,74,0,0,27.6,0.244,40,0
+7,194,68,28,0,35.9,0.745,41,1
+8,181,68,36,495,30.1,0.615,60,1
+1,128,98,41,58,32,1.321,33,1
+8,109,76,39,114,27.9,0.64,31,1
+5,139,80,35,160,31.6,0.361,25,1
+3,111,62,0,0,22.6,0.142,21,0
+9,123,70,44,94,33.1,0.374,40,0
+7,159,66,0,0,30.4,0.383,36,1
+11,135,0,0,0,52.3,0.578,40,1
+8,85,55,20,0,24.4,0.136,42,0
+5,158,84,41,210,39.4,0.395,29,1
+1,105,58,0,0,24.3,0.187,21,0
+3,107,62,13,48,22.9,0.678,23,1
+4,109,64,44,99,34.8,0.905,26,1
+4,148,60,27,318,30.9,0.15,29,1
+0,113,80,16,0,31,0.874,21,0
+1,138,82,0,0,40.1,0.236,28,0
+0,108,68,20,0,27.3,0.787,32,0
+2,99,70,16,44,20.4,0.235,27,0
+6,103,72,32,190,37.7,0.324,55,0
+5,111,72,28,0,23.9,0.407,27,0
+8,196,76,29,280,37.5,0.605,57,1
+5,162,104,0,0,37.7,0.151,52,1
+1,96,64,27,87,33.2,0.289,21,0
+7,184,84,33,0,35.5,0.355,41,1
+2,81,60,22,0,27.7,0.29,25,0
+0,147,85,54,0,42.8,0.375,24,0
+7,179,95,31,0,34.2,0.164,60,0
+0,140,65,26,130,42.6,0.431,24,1
+9,112,82,32,175,34.2,0.26,36,1
+12,151,70,40,271,41.8,0.742,38,1
+5,109,62,41,129,35.8,0.514,25,1
+6,125,68,30,120,30,0.464,32,0
+5,85,74,22,0,29,1.224,32,1
+5,112,66,0,0,37.8,0.261,41,1
+0,177,60,29,478,34.6,1.072,21,1
+2,158,90,0,0,31.6,0.805,66,1
+7,119,0,0,0,25.2,0.209,37,0
+7,142,60,33,190,28.8,0.687,61,0
+1,100,66,15,56,23.6,0.666,26,0
+1,87,78,27,32,34.6,0.101,22,0
+0,101,76,0,0,35.7,0.198,26,0
+3,162,52,38,0,37.2,0.652,24,1
+4,197,70,39,744,36.7,2.329,31,0
+0,117,80,31,53,45.2,0.089,24,0
+4,142,86,0,0,44,0.645,22,1
+6,134,80,37,370,46.2,0.238,46,1
+1,79,80,25,37,25.4,0.583,22,0
+4,122,68,0,0,35,0.394,29,0
+3,74,68,28,45,29.7,0.293,23,0
+4,171,72,0,0,43.6,0.479,26,1
+7,181,84,21,192,35.9,0.586,51,1
+0,179,90,27,0,44.1,0.686,23,1
+9,164,84,21,0,30.8,0.831,32,1
+0,104,76,0,0,18.4,0.582,27,0
+1,91,64,24,0,29.2,0.192,21,0
+4,91,70,32,88,33.1,0.446,22,0
+3,139,54,0,0,25.6,0.402,22,1
+6,119,50,22,176,27.1,1.318,33,1
+2,146,76,35,194,38.2,0.329,29,0
+9,184,85,15,0,30,1.213,49,1
+10,122,68,0,0,31.2,0.258,41,0
+0,165,90,33,680,52.3,0.427,23,0
+9,124,70,33,402,35.4,0.282,34,0
+1,111,86,19,0,30.1,0.143,23,0
+9,106,52,0,0,31.2,0.38,42,0
+2,129,84,0,0,28,0.284,27,0
+2,90,80,14,55,24.4,0.249,24,0
+0,86,68,32,0,35.8,0.238,25,0
+12,92,62,7,258,27.6,0.926,44,1
+1,113,64,35,0,33.6,0.543,21,1
+3,111,56,39,0,30.1,0.557,30,0
+2,114,68,22,0,28.7,0.092,25,0
+1,193,50,16,375,25.9,0.655,24,0
+11,155,76,28,150,33.3,1.353,51,1
+3,191,68,15,130,30.9,0.299,34,0
+3,141,0,0,0,30,0.761,27,1
+4,95,70,32,0,32.1,0.612,24,0
+3,142,80,15,0,32.4,0.2,63,0
+4,123,62,0,0,32,0.226,35,1
+5,96,74,18,67,33.6,0.997,43,0
+0,138,0,0,0,36.3,0.933,25,1
+2,128,64,42,0,40,1.101,24,0
+0,102,52,0,0,25.1,0.078,21,0
+2,146,0,0,0,27.5,0.24,28,1
+10,101,86,37,0,45.6,1.136,38,1
+2,108,62,32,56,25.2,0.128,21,0
+3,122,78,0,0,23,0.254,40,0
+1,71,78,50,45,33.2,0.422,21,0
+13,106,70,0,0,34.2,0.251,52,0
+2,100,70,52,57,40.5,0.677,25,0
+7,106,60,24,0,26.5,0.296,29,1
+0,104,64,23,116,27.8,0.454,23,0
+5,114,74,0,0,24.9,0.744,57,0
+2,108,62,10,278,25.3,0.881,22,0
+0,146,70,0,0,37.9,0.334,28,1
+10,129,76,28,122,35.9,0.28,39,0
+7,133,88,15,155,32.4,0.262,37,0
+7,161,86,0,0,30.4,0.165,47,1
+2,108,80,0,0,27,0.259,52,1
+7,136,74,26,135,26,0.647,51,0
+5,155,84,44,545,38.7,0.619,34,0
+1,119,86,39,220,45.6,0.808,29,1
+4,96,56,17,49,20.8,0.34,26,0
+5,108,72,43,75,36.1,0.263,33,0
+0,78,88,29,40,36.9,0.434,21,0
+0,107,62,30,74,36.6,0.757,25,1
+2,128,78,37,182,43.3,1.224,31,1
+1,128,48,45,194,40.5,0.613,24,1
+0,161,50,0,0,21.9,0.254,65,0
+6,151,62,31,120,35.5,0.692,28,0
+2,146,70,38,360,28,0.337,29,1
+0,126,84,29,215,30.7,0.52,24,0
+14,100,78,25,184,36.6,0.412,46,1
+8,112,72,0,0,23.6,0.84,58,0
+0,167,0,0,0,32.3,0.839,30,1
+2,144,58,33,135,31.6,0.422,25,1
+5,77,82,41,42,35.8,0.156,35,0
+5,115,98,0,0,52.9,0.209,28,1
+3,150,76,0,0,21,0.207,37,0
+2,120,76,37,105,39.7,0.215,29,0
+10,161,68,23,132,25.5,0.326,47,1
+0,137,68,14,148,24.8,0.143,21,0
+0,128,68,19,180,30.5,1.391,25,1
+2,124,68,28,205,32.9,0.875,30,1
+6,80,66,30,0,26.2,0.313,41,0
+0,106,70,37,148,39.4,0.605,22,0
+2,155,74,17,96,26.6,0.433,27,1
+3,113,50,10,85,29.5,0.626,25,0
+7,109,80,31,0,35.9,1.127,43,1
+2,112,68,22,94,34.1,0.315,26,0
+3,99,80,11,64,19.3,0.284,30,0
+3,182,74,0,0,30.5,0.345,29,1
+3,115,66,39,140,38.1,0.15,28,0
+6,194,78,0,0,23.5,0.129,59,1
+4,129,60,12,231,27.5,0.527,31,0
+3,112,74,30,0,31.6,0.197,25,1
+0,124,70,20,0,27.4,0.254,36,1
+13,152,90,33,29,26.8,0.731,43,1
+2,112,75,32,0,35.7,0.148,21,0
+1,157,72,21,168,25.6,0.123,24,0
+1,122,64,32,156,35.1,0.692,30,1
+10,179,70,0,0,35.1,0.2,37,0
+2,102,86,36,120,45.5,0.127,23,1
+6,105,70,32,68,30.8,0.122,37,0
+8,118,72,19,0,23.1,1.476,46,0
+2,87,58,16,52,32.7,0.166,25,0
+1,180,0,0,0,43.3,0.282,41,1
+12,106,80,0,0,23.6,0.137,44,0
+1,95,60,18,58,23.9,0.26,22,0
+0,165,76,43,255,47.9,0.259,26,0
+0,117,0,0,0,33.8,0.932,44,0
+5,115,76,0,0,31.2,0.343,44,1
+9,152,78,34,171,34.2,0.893,33,1
+7,178,84,0,0,39.9,0.331,41,1
+1,130,70,13,105,25.9,0.472,22,0
+1,95,74,21,73,25.9,0.673,36,0
+1,0,68,35,0,32,0.389,22,0
+5,122,86,0,0,34.7,0.29,33,0
+8,95,72,0,0,36.8,0.485,57,0
+8,126,88,36,108,38.5,0.349,49,0
+1,139,46,19,83,28.7,0.654,22,0
+3,116,0,0,0,23.5,0.187,23,0
+3,99,62,19,74,21.8,0.279,26,0
+5,0,80,32,0,41,0.346,37,1
+4,92,80,0,0,42.2,0.237,29,0
+4,137,84,0,0,31.2,0.252,30,0
+3,61,82,28,0,34.4,0.243,46,0
+1,90,62,12,43,27.2,0.58,24,0
+3,90,78,0,0,42.7,0.559,21,0
+9,165,88,0,0,30.4,0.302,49,1
+1,125,50,40,167,33.3,0.962,28,1
+13,129,0,30,0,39.9,0.569,44,1
+12,88,74,40,54,35.3,0.378,48,0
+1,196,76,36,249,36.5,0.875,29,1
+5,189,64,33,325,31.2,0.583,29,1
+5,158,70,0,0,29.8,0.207,63,0
+5,103,108,37,0,39.2,0.305,65,0
+4,146,78,0,0,38.5,0.52,67,1
+4,147,74,25,293,34.9,0.385,30,0
+5,99,54,28,83,34,0.499,30,0
+6,124,72,0,0,27.6,0.368,29,1
+0,101,64,17,0,21,0.252,21,0
+3,81,86,16,66,27.5,0.306,22,0
+1,133,102,28,140,32.8,0.234,45,1
+3,173,82,48,465,38.4,2.137,25,1
+0,118,64,23,89,0,1.731,21,0
+0,84,64,22,66,35.8,0.545,21,0
+2,105,58,40,94,34.9,0.225,25,0
+2,122,52,43,158,36.2,0.816,28,0
+12,140,82,43,325,39.2,0.528,58,1
+0,98,82,15,84,25.2,0.299,22,0
+1,87,60,37,75,37.2,0.509,22,0
+4,156,75,0,0,48.3,0.238,32,1
+0,93,100,39,72,43.4,1.021,35,0
+1,107,72,30,82,30.8,0.821,24,0
+0,105,68,22,0,20,0.236,22,0
+1,109,60,8,182,25.4,0.947,21,0
+1,90,62,18,59,25.1,1.268,25,0
+1,125,70,24,110,24.3,0.221,25,0
+1,119,54,13,50,22.3,0.205,24,0
+5,116,74,29,0,32.3,0.66,35,1
+8,105,100,36,0,43.3,0.239,45,1
+5,144,82,26,285,32,0.452,58,1
+3,100,68,23,81,31.6,0.949,28,0
+1,100,66,29,196,32,0.444,42,0
+5,166,76,0,0,45.7,0.34,27,1
+1,131,64,14,415,23.7,0.389,21,0
+4,116,72,12,87,22.1,0.463,37,0
+4,158,78,0,0,32.9,0.803,31,1
+2,127,58,24,275,27.7,1.6,25,0
+3,96,56,34,115,24.7,0.944,39,0
+0,131,66,40,0,34.3,0.196,22,1
+3,82,70,0,0,21.1,0.389,25,0
+3,193,70,31,0,34.9,0.241,25,1
+4,95,64,0,0,32,0.161,31,1
+6,137,61,0,0,24.2,0.151,55,0
+5,136,84,41,88,35,0.286,35,1
+9,72,78,25,0,31.6,0.28,38,0
+5,168,64,0,0,32.9,0.135,41,1
+2,123,48,32,165,42.1,0.52,26,0
+4,115,72,0,0,28.9,0.376,46,1
+0,101,62,0,0,21.9,0.336,25,0
+8,197,74,0,0,25.9,1.191,39,1
+1,172,68,49,579,42.4,0.702,28,1
+6,102,90,39,0,35.7,0.674,28,0
+1,112,72,30,176,34.4,0.528,25,0
+1,143,84,23,310,42.4,1.076,22,0
+1,143,74,22,61,26.2,0.256,21,0
+0,138,60,35,167,34.6,0.534,21,1
+3,173,84,33,474,35.7,0.258,22,1
+1,97,68,21,0,27.2,1.095,22,0
+4,144,82,32,0,38.5,0.554,37,1
+1,83,68,0,0,18.2,0.624,27,0
+3,129,64,29,115,26.4,0.219,28,1
+1,119,88,41,170,45.3,0.507,26,0
+2,94,68,18,76,26,0.561,21,0
+0,102,64,46,78,40.6,0.496,21,0
+2,115,64,22,0,30.8,0.421,21,0
+8,151,78,32,210,42.9,0.516,36,1
+4,184,78,39,277,37,0.264,31,1
+0,94,0,0,0,0,0.256,25,0
+1,181,64,30,180,34.1,0.328,38,1
+0,135,94,46,145,40.6,0.284,26,0
+1,95,82,25,180,35,0.233,43,1
+2,99,0,0,0,22.2,0.108,23,0
+3,89,74,16,85,30.4,0.551,38,0
+1,80,74,11,60,30,0.527,22,0
+2,139,75,0,0,25.6,0.167,29,0
+1,90,68,8,0,24.5,1.138,36,0
+0,141,0,0,0,42.4,0.205,29,1
+12,140,85,33,0,37.4,0.244,41,0
+5,147,75,0,0,29.9,0.434,28,0
+1,97,70,15,0,18.2,0.147,21,0
+6,107,88,0,0,36.8,0.727,31,0
+0,189,104,25,0,34.3,0.435,41,1
+2,83,66,23,50,32.2,0.497,22,0
+4,117,64,27,120,33.2,0.23,24,0
+8,108,70,0,0,30.5,0.955,33,1
+4,117,62,12,0,29.7,0.38,30,1
+0,180,78,63,14,59.4,2.42,25,1
+1,100,72,12,70,25.3,0.658,28,0
+0,95,80,45,92,36.5,0.33,26,0
+0,104,64,37,64,33.6,0.51,22,1
+0,120,74,18,63,30.5,0.285,26,0
+1,82,64,13,95,21.2,0.415,23,0
+2,134,70,0,0,28.9,0.542,23,1
+0,91,68,32,210,39.9,0.381,25,0
+2,119,0,0,0,19.6,0.832,72,0
+2,100,54,28,105,37.8,0.498,24,0
+14,175,62,30,0,33.6,0.212,38,1
+1,135,54,0,0,26.7,0.687,62,0
+5,86,68,28,71,30.2,0.364,24,0
+10,148,84,48,237,37.6,1.001,51,1
+9,134,74,33,60,25.9,0.46,81,0
+9,120,72,22,56,20.8,0.733,48,0
+1,71,62,0,0,21.8,0.416,26,0
+8,74,70,40,49,35.3,0.705,39,0
+5,88,78,30,0,27.6,0.258,37,0
+10,115,98,0,0,24,1.022,34,0
+0,124,56,13,105,21.8,0.452,21,0
+0,74,52,10,36,27.8,0.269,22,0
+0,97,64,36,100,36.8,0.6,25,0
+8,120,0,0,0,30,0.183,38,1
+6,154,78,41,140,46.1,0.571,27,0
+1,144,82,40,0,41.3,0.607,28,0
+0,137,70,38,0,33.2,0.17,22,0
+0,119,66,27,0,38.8,0.259,22,0
+7,136,90,0,0,29.9,0.21,50,0
+4,114,64,0,0,28.9,0.126,24,0
+0,137,84,27,0,27.3,0.231,59,0
+2,105,80,45,191,33.7,0.711,29,1
+7,114,76,17,110,23.8,0.466,31,0
+8,126,74,38,75,25.9,0.162,39,0
+4,132,86,31,0,28,0.419,63,0
+3,158,70,30,328,35.5,0.344,35,1
+0,123,88,37,0,35.2,0.197,29,0
+4,85,58,22,49,27.8,0.306,28,0
+0,84,82,31,125,38.2,0.233,23,0
+0,145,0,0,0,44.2,0.63,31,1
+0,135,68,42,250,42.3,0.365,24,1
+1,139,62,41,480,40.7,0.536,21,0
+0,173,78,32,265,46.5,1.159,58,0
+4,99,72,17,0,25.6,0.294,28,0
+8,194,80,0,0,26.1,0.551,67,0
+2,83,65,28,66,36.8,0.629,24,0
+2,89,90,30,0,33.5,0.292,42,0
+4,99,68,38,0,32.8,0.145,33,0
+4,125,70,18,122,28.9,1.144,45,1
+3,80,0,0,0,0,0.174,22,0
+6,166,74,0,0,26.6,0.304,66,0
+5,110,68,0,0,26,0.292,30,0
+2,81,72,15,76,30.1,0.547,25,0
+7,195,70,33,145,25.1,0.163,55,1
+6,154,74,32,193,29.3,0.839,39,0
+2,117,90,19,71,25.2,0.313,21,0
+3,84,72,32,0,37.2,0.267,28,0
+6,0,68,41,0,39,0.727,41,1
+7,94,64,25,79,33.3,0.738,41,0
+3,96,78,39,0,37.3,0.238,40,0
+10,75,82,0,0,33.3,0.263,38,0
+0,180,90,26,90,36.5,0.314,35,1
+1,130,60,23,170,28.6,0.692,21,0
+2,84,50,23,76,30.4,0.968,21,0
+8,120,78,0,0,25,0.409,64,0
+12,84,72,31,0,29.7,0.297,46,1
+0,139,62,17,210,22.1,0.207,21,0
+9,91,68,0,0,24.2,0.2,58,0
+2,91,62,0,0,27.3,0.525,22,0
+3,99,54,19,86,25.6,0.154,24,0
+3,163,70,18,105,31.6,0.268,28,1
+9,145,88,34,165,30.3,0.771,53,1
+7,125,86,0,0,37.6,0.304,51,0
+13,76,60,0,0,32.8,0.18,41,0
+6,129,90,7,326,19.6,0.582,60,0
+2,68,70,32,66,25,0.187,25,0
+3,124,80,33,130,33.2,0.305,26,0
+6,114,0,0,0,0,0.189,26,0
+9,130,70,0,0,34.2,0.652,45,1
+3,125,58,0,0,31.6,0.151,24,0
+3,87,60,18,0,21.8,0.444,21,0
+1,97,64,19,82,18.2,0.299,21,0
+3,116,74,15,105,26.3,0.107,24,0
+0,117,66,31,188,30.8,0.493,22,0
+0,111,65,0,0,24.6,0.66,31,0
+2,122,60,18,106,29.8,0.717,22,0
+0,107,76,0,0,45.3,0.686,24,0
+1,86,66,52,65,41.3,0.917,29,0
+6,91,0,0,0,29.8,0.501,31,0
+1,77,56,30,56,33.3,1.251,24,0
+4,132,0,0,0,32.9,0.302,23,1
+0,105,90,0,0,29.6,0.197,46,0
+0,57,60,0,0,21.7,0.735,67,0
+0,127,80,37,210,36.3,0.804,23,0
+3,129,92,49,155,36.4,0.968,32,1
+8,100,74,40,215,39.4,0.661,43,1
+3,128,72,25,190,32.4,0.549,27,1
+10,90,85,32,0,34.9,0.825,56,1
+4,84,90,23,56,39.5,0.159,25,0
+1,88,78,29,76,32,0.365,29,0
+8,186,90,35,225,34.5,0.423,37,1
+5,187,76,27,207,43.6,1.034,53,1
+4,131,68,21,166,33.1,0.16,28,0
+1,164,82,43,67,32.8,0.341,50,0
+4,189,110,31,0,28.5,0.68,37,0
+1,116,70,28,0,27.4,0.204,21,0
+3,84,68,30,106,31.9,0.591,25,0
+6,114,88,0,0,27.8,0.247,66,0
+1,88,62,24,44,29.9,0.422,23,0
+1,84,64,23,115,36.9,0.471,28,0
+7,124,70,33,215,25.5,0.161,37,0
+1,97,70,40,0,38.1,0.218,30,0
+8,110,76,0,0,27.8,0.237,58,0
+11,103,68,40,0,46.2,0.126,42,0
+11,85,74,0,0,30.1,0.3,35,0
+6,125,76,0,0,33.8,0.121,54,1
+0,198,66,32,274,41.3,0.502,28,1
+1,87,68,34,77,37.6,0.401,24,0
+6,99,60,19,54,26.9,0.497,32,0
+0,91,80,0,0,32.4,0.601,27,0
+2,95,54,14,88,26.1,0.748,22,0
+1,99,72,30,18,38.6,0.412,21,0
+6,92,62,32,126,32,0.085,46,0
+4,154,72,29,126,31.3,0.338,37,0
+0,121,66,30,165,34.3,0.203,33,1
+3,78,70,0,0,32.5,0.27,39,0
+2,130,96,0,0,22.6,0.268,21,0
+3,111,58,31,44,29.5,0.43,22,0
+2,98,60,17,120,34.7,0.198,22,0
+1,143,86,30,330,30.1,0.892,23,0
+1,119,44,47,63,35.5,0.28,25,0
+6,108,44,20,130,24,0.813,35,0
+2,118,80,0,0,42.9,0.693,21,1
+10,133,68,0,0,27,0.245,36,0
+2,197,70,99,0,34.7,0.575,62,1
+0,151,90,46,0,42.1,0.371,21,1
+6,109,60,27,0,25,0.206,27,0
+12,121,78,17,0,26.5,0.259,62,0
+8,100,76,0,0,38.7,0.19,42,0
+8,124,76,24,600,28.7,0.687,52,1
+1,93,56,11,0,22.5,0.417,22,0
+8,143,66,0,0,34.9,0.129,41,1
+6,103,66,0,0,24.3,0.249,29,0
+3,176,86,27,156,33.3,1.154,52,1
+0,73,0,0,0,21.1,0.342,25,0
+11,111,84,40,0,46.8,0.925,45,1
+2,112,78,50,140,39.4,0.175,24,0
+3,132,80,0,0,34.4,0.402,44,1
+2,82,52,22,115,28.5,1.699,25,0
+6,123,72,45,230,33.6,0.733,34,0
+0,188,82,14,185,32,0.682,22,1
+0,67,76,0,0,45.3,0.194,46,0
+1,89,24,19,25,27.8,0.559,21,0
+1,173,74,0,0,36.8,0.088,38,1
+1,109,38,18,120,23.1,0.407,26,0
+1,108,88,19,0,27.1,0.4,24,0
+6,96,0,0,0,23.7,0.19,28,0
+1,124,74,36,0,27.8,0.1,30,0
+7,150,78,29,126,35.2,0.692,54,1
+4,183,0,0,0,28.4,0.212,36,1
+1,124,60,32,0,35.8,0.514,21,0
+1,181,78,42,293,40,1.258,22,1
+1,92,62,25,41,19.5,0.482,25,0
+0,152,82,39,272,41.5,0.27,27,0
+1,111,62,13,182,24,0.138,23,0
+3,106,54,21,158,30.9,0.292,24,0
+3,174,58,22,194,32.9,0.593,36,1
+7,168,88,42,321,38.2,0.787,40,1
+6,105,80,28,0,32.5,0.878,26,0
+11,138,74,26,144,36.1,0.557,50,1
+3,106,72,0,0,25.8,0.207,27,0
+6,117,96,0,0,28.7,0.157,30,0
+2,68,62,13,15,20.1,0.257,23,0
+9,112,82,24,0,28.2,1.282,50,1
+0,119,0,0,0,32.4,0.141,24,1
+2,112,86,42,160,38.4,0.246,28,0
+2,92,76,20,0,24.2,1.698,28,0
+6,183,94,0,0,40.8,1.461,45,0
+0,94,70,27,115,43.5,0.347,21,0
+2,108,64,0,0,30.8,0.158,21,0
+4,90,88,47,54,37.7,0.362,29,0
+0,125,68,0,0,24.7,0.206,21,0
+0,132,78,0,0,32.4,0.393,21,0
+5,128,80,0,0,34.6,0.144,45,0
+4,94,65,22,0,24.7,0.148,21,0
+7,114,64,0,0,27.4,0.732,34,1
+0,102,78,40,90,34.5,0.238,24,0
+2,111,60,0,0,26.2,0.343,23,0
+1,128,82,17,183,27.5,0.115,22,0
+10,92,62,0,0,25.9,0.167,31,0
+13,104,72,0,0,31.2,0.465,38,1
+5,104,74,0,0,28.8,0.153,48,0
+2,94,76,18,66,31.6,0.649,23,0
+7,97,76,32,91,40.9,0.871,32,1
+1,100,74,12,46,19.5,0.149,28,0
+0,102,86,17,105,29.3,0.695,27,0
+4,128,70,0,0,34.3,0.303,24,0
+6,147,80,0,0,29.5,0.178,50,1
+4,90,0,0,0,28,0.61,31,0
+3,103,72,30,152,27.6,0.73,27,0
+2,157,74,35,440,39.4,0.134,30,0
+1,167,74,17,144,23.4,0.447,33,1
+0,179,50,36,159,37.8,0.455,22,1
+11,136,84,35,130,28.3,0.26,42,1
+0,107,60,25,0,26.4,0.133,23,0
+1,91,54,25,100,25.2,0.234,23,0
+1,117,60,23,106,33.8,0.466,27,0
+5,123,74,40,77,34.1,0.269,28,0
+2,120,54,0,0,26.8,0.455,27,0
+1,106,70,28,135,34.2,0.142,22,0
+2,155,52,27,540,38.7,0.24,25,1
+2,101,58,35,90,21.8,0.155,22,0
+1,120,80,48,200,38.9,1.162,41,0
+11,127,106,0,0,39,0.19,51,0
+3,80,82,31,70,34.2,1.292,27,1
+10,162,84,0,0,27.7,0.182,54,0
+1,199,76,43,0,42.9,1.394,22,1
+8,167,106,46,231,37.6,0.165,43,1
+9,145,80,46,130,37.9,0.637,40,1
+6,115,60,39,0,33.7,0.245,40,1
+1,112,80,45,132,34.8,0.217,24,0
+4,145,82,18,0,32.5,0.235,70,1
+10,111,70,27,0,27.5,0.141,40,1
+6,98,58,33,190,34,0.43,43,0
+9,154,78,30,100,30.9,0.164,45,0
+6,165,68,26,168,33.6,0.631,49,0
+1,99,58,10,0,25.4,0.551,21,0
+10,68,106,23,49,35.5,0.285,47,0
+3,123,100,35,240,57.3,0.88,22,0
+8,91,82,0,0,35.6,0.587,68,0
+6,195,70,0,0,30.9,0.328,31,1
+9,156,86,0,0,24.8,0.23,53,1
+0,93,60,0,0,35.3,0.263,25,0
+3,121,52,0,0,36,0.127,25,1
+2,101,58,17,265,24.2,0.614,23,0
+2,56,56,28,45,24.2,0.332,22,0
+0,162,76,36,0,49.6,0.364,26,1
+0,95,64,39,105,44.6,0.366,22,0
+4,125,80,0,0,32.3,0.536,27,1
+5,136,82,0,0,0,0.64,69,0
+2,129,74,26,205,33.2,0.591,25,0
+3,130,64,0,0,23.1,0.314,22,0
+1,107,50,19,0,28.3,0.181,29,0
+1,140,74,26,180,24.1,0.828,23,0
+1,144,82,46,180,46.1,0.335,46,1
+8,107,80,0,0,24.6,0.856,34,0
+13,158,114,0,0,42.3,0.257,44,1
+2,121,70,32,95,39.1,0.886,23,0
+7,129,68,49,125,38.5,0.439,43,1
+2,90,60,0,0,23.5,0.191,25,0
+7,142,90,24,480,30.4,0.128,43,1
+3,169,74,19,125,29.9,0.268,31,1
+0,99,0,0,0,25,0.253,22,0
+4,127,88,11,155,34.5,0.598,28,0
+4,118,70,0,0,44.5,0.904,26,0
+2,122,76,27,200,35.9,0.483,26,0
+6,125,78,31,0,27.6,0.565,49,1
+1,168,88,29,0,35,0.905,52,1
+2,129,0,0,0,38.5,0.304,41,0
+4,110,76,20,100,28.4,0.118,27,0
+6,80,80,36,0,39.8,0.177,28,0
+10,115,0,0,0,0,0.261,30,1
+2,127,46,21,335,34.4,0.176,22,0
+9,164,78,0,0,32.8,0.148,45,1
+2,93,64,32,160,38,0.674,23,1
+3,158,64,13,387,31.2,0.295,24,0
+5,126,78,27,22,29.6,0.439,40,0
+10,129,62,36,0,41.2,0.441,38,1
+0,134,58,20,291,26.4,0.352,21,0
+3,102,74,0,0,29.5,0.121,32,0
+7,187,50,33,392,33.9,0.826,34,1
+3,173,78,39,185,33.8,0.97,31,1
+10,94,72,18,0,23.1,0.595,56,0
+1,108,60,46,178,35.5,0.415,24,0
+5,97,76,27,0,35.6,0.378,52,1
+4,83,86,19,0,29.3,0.317,34,0
+1,114,66,36,200,38.1,0.289,21,0
+1,149,68,29,127,29.3,0.349,42,1
+5,117,86,30,105,39.1,0.251,42,0
+1,111,94,0,0,32.8,0.265,45,0
+4,112,78,40,0,39.4,0.236,38,0
+1,116,78,29,180,36.1,0.496,25,0
+0,141,84,26,0,32.4,0.433,22,0
+2,175,88,0,0,22.9,0.326,22,0
+2,92,52,0,0,30.1,0.141,22,0
+3,130,78,23,79,28.4,0.323,34,1
+8,120,86,0,0,28.4,0.259,22,1
+2,174,88,37,120,44.5,0.646,24,1
+2,106,56,27,165,29,0.426,22,0
+2,105,75,0,0,23.3,0.56,53,0
+4,95,60,32,0,35.4,0.284,28,0
+0,126,86,27,120,27.4,0.515,21,0
+8,65,72,23,0,32,0.6,42,0
+2,99,60,17,160,36.6,0.453,21,0
+1,102,74,0,0,39.5,0.293,42,1
+11,120,80,37,150,42.3,0.785,48,1
+3,102,44,20,94,30.8,0.4,26,0
+1,109,58,18,116,28.5,0.219,22,0
+9,140,94,0,0,32.7,0.734,45,1
+13,153,88,37,140,40.6,1.174,39,0
+12,100,84,33,105,30,0.488,46,0
+1,147,94,41,0,49.3,0.358,27,1
+1,81,74,41,57,46.3,1.096,32,0
+3,187,70,22,200,36.4,0.408,36,1
+6,162,62,0,0,24.3,0.178,50,1
+4,136,70,0,0,31.2,1.182,22,1
+1,121,78,39,74,39,0.261,28,0
+3,108,62,24,0,26,0.223,25,0
+0,181,88,44,510,43.3,0.222,26,1
+8,154,78,32,0,32.4,0.443,45,1
+1,128,88,39,110,36.5,1.057,37,1
+7,137,90,41,0,32,0.391,39,0
+0,123,72,0,0,36.3,0.258,52,1
+1,106,76,0,0,37.5,0.197,26,0
+6,190,92,0,0,35.5,0.278,66,1
+2,88,58,26,16,28.4,0.766,22,0
+9,170,74,31,0,44,0.403,43,1
+9,89,62,0,0,22.5,0.142,33,0
+10,101,76,48,180,32.9,0.171,63,0
+2,122,70,27,0,36.8,0.34,27,0
+5,121,72,23,112,26.2,0.245,30,0
+1,126,60,0,0,30.1,0.349,47,1
+1,93,70,31,0,30.4,0.315,23,0

diabetes/diabetes_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3494e63c66fca442559285dd50edf897718d71be3efa0455ca18f25ef5ae9693
+size 11435

hypertension/extratrees_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25d68f043edf9b7102ec5d20d92dd7ff22e825a2c6db7953c0528c717694dc39
+size 13593275

hypertension/scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:002f9ab7fbeacedff9081497368489816e48e302c79ca20a080f4b497a36aad5
+size 802

mental/MentalH.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c181695a3ccfd5f4654b32b84aac405a5a0d615369bfdb101d56b9797c510382
+size 1934069

requirements.txt

@@ -0,0 +1,17 @@
+numpy
+scikit-learn
+streamlit
+streamlit-option-menu
+seaborn
+matplotlib
+pandas
+google-generativeai
+python-dotenv
+torch 
+transformers
+SpeechRecognition
+pydub
+tf-keras
+plotly
+plotly-express
+

sleep_health/Sleep_health_and_lifestyle_dataset.csv

@@ -0,0 +1,375 @@
+Person ID,Gender,Age,Occupation,Sleep Duration,Quality of Sleep,Physical Activity Level,Stress Level,BMI Category,Blood Pressure,Heart Rate,Daily Steps,Sleep Disorder
+1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126/83,77,4200,None
+2,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+3,Male,28,Doctor,6.2,6,60,8,Normal,125/80,75,10000,None
+4,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+5,Male,28,Sales Representative,5.9,4,30,8,Obese,140/90,85,3000,Sleep Apnea
+6,Male,28,Software Engineer,5.9,4,30,8,Obese,140/90,85,3000,Insomnia
+7,Male,29,Teacher,6.3,6,40,7,Obese,140/90,82,3500,Insomnia
+8,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+9,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+10,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+11,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+12,Male,29,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+13,Male,29,Doctor,6.1,6,30,8,Normal,120/80,70,8000,None
+14,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+15,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+16,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,None
+17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Sleep Apnea
+18,Male,29,Doctor,6,6,30,8,Normal,120/80,70,8000,Sleep Apnea
+19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132/87,80,4000,Insomnia
+20,Male,30,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+21,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+22,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+23,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+24,Male,30,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+25,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+26,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+27,Male,30,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+28,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+29,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+30,Male,30,Doctor,7.9,7,75,6,Normal,120/80,70,8000,None
+31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Sleep Apnea
+32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130/86,78,4100,Insomnia
+33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117/76,69,6800,None
+34,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+35,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+36,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+37,Male,31,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+38,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+39,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+40,Male,31,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+41,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+42,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+43,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+44,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+45,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+46,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+47,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+48,Male,31,Doctor,7.8,7,75,6,Normal,120/80,70,8000,None
+49,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+50,Male,31,Doctor,7.7,7,75,6,Normal,120/80,70,8000,Sleep Apnea
+51,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+52,Male,32,Engineer,7.5,8,45,3,Normal,120/80,70,8000,None
+53,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+54,Male,32,Doctor,7.6,7,75,6,Normal,120/80,70,8000,None
+55,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+56,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+57,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+58,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+59,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+60,Male,32,Doctor,7.7,7,75,6,Normal,120/80,70,8000,None
+61,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+62,Male,32,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+63,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+64,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+65,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+66,Male,32,Doctor,6.2,6,30,8,Normal,125/80,72,5000,None
+67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118/76,68,7000,None
+68,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,Insomnia
+69,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+70,Female,33,Scientist,6.2,6,50,6,Overweight,128/85,76,5500,None
+71,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+72,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+73,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+74,Male,33,Doctor,6.1,6,30,8,Normal,125/80,72,5000,None
+75,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+76,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+77,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+78,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+79,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+80,Male,33,Doctor,6,6,30,8,Normal,125/80,72,5000,None
+81,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+82,Female,34,Scientist,5.8,4,32,8,Overweight,131/86,81,5200,Sleep Apnea
+83,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+84,Male,35,Teacher,6.7,7,40,5,Overweight,128/84,70,5600,None
+85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+86,Female,35,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+87,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+88,Male,35,Engineer,7.2,8,60,4,Normal,125/80,65,5000,None
+89,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+90,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+91,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+92,Male,35,Engineer,7.3,8,60,4,Normal,125/80,65,5000,None
+93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120/80,70,8000,None
+94,Male,35,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+95,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,Insomnia
+96,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+97,Female,36,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+98,Female,36,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+99,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+100,Female,36,Teacher,7.1,8,60,4,Normal,115/75,68,7000,None
+101,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+102,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+103,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,None
+104,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Sleep Apnea
+105,Female,36,Teacher,7.2,8,60,4,Normal,115/75,68,7000,Sleep Apnea
+106,Male,36,Teacher,6.6,5,35,7,Overweight,129/84,74,4800,Insomnia
+107,Female,37,Nurse,6.1,6,42,6,Overweight,126/83,77,4200,None
+108,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+109,Male,37,Engineer,7.8,8,70,4,Normal Weight,120/80,68,7000,None
+110,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+111,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+112,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+113,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+114,Male,37,Lawyer,7.4,8,60,5,Normal,130/85,68,8000,None
+115,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+116,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+117,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+118,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+119,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+120,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+121,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+122,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+123,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+124,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+125,Female,37,Accountant,7.2,8,60,4,Normal,115/75,68,7000,None
+126,Female,37,Nurse,7.5,8,60,4,Normal Weight,120/80,70,8000,None
+127,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+128,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+129,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+130,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+131,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+132,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+133,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+134,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+135,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+136,Male,38,Lawyer,7.3,8,60,5,Normal,130/85,68,8000,None
+137,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+138,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+139,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+140,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+141,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+142,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,None
+143,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+144,Female,38,Accountant,7.1,8,60,4,Normal,115/75,68,7000,None
+145,Male,38,Lawyer,7.1,8,60,5,Normal,130/85,68,8000,Sleep Apnea
+146,Female,38,Lawyer,7.4,7,60,5,Obese,135/88,84,3300,Sleep Apnea
+147,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,Insomnia
+148,Male,39,Engineer,6.5,5,40,7,Overweight,132/87,80,4000,Insomnia
+149,Female,39,Lawyer,6.9,7,50,6,Normal Weight,128/85,75,5500,None
+150,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+151,Female,39,Accountant,8,9,80,3,Normal Weight,115/78,67,7500,None
+152,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+153,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+154,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+155,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+156,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+157,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+158,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+159,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+160,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+161,Male,39,Lawyer,7.2,8,60,5,Normal,130/85,68,8000,None
+162,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+163,Female,40,Accountant,7.2,8,55,6,Normal Weight,119/77,73,7300,None
+164,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+165,Male,40,Lawyer,7.9,8,90,5,Normal,130/85,68,8000,None
+166,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,Insomnia
+167,Male,41,Engineer,7.3,8,70,6,Normal Weight,121/79,72,6200,None
+168,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+169,Male,41,Lawyer,7.1,7,55,6,Overweight,125/82,72,6000,None
+170,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+171,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+172,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+173,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+174,Male,41,Lawyer,7.7,8,90,5,Normal,130/85,70,8000,None
+175,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+176,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+177,Male,41,Lawyer,7.6,8,90,5,Normal,130/85,70,8000,None
+178,Male,42,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+179,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+180,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+181,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+182,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+183,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+184,Male,42,Lawyer,7.8,8,90,5,Normal,130/85,70,8000,None
+185,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+186,Female,42,Teacher,6.8,6,45,7,Overweight,130/85,78,5000,Sleep Apnea
+187,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+188,Male,43,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+189,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+190,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+191,Female,43,Teacher,6.7,7,45,4,Overweight,135/90,65,6000,Insomnia
+192,Male,43,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+193,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+194,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+195,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+196,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+197,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+198,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+199,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+200,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+201,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Insomnia
+202,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+203,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Insomnia
+204,Male,43,Engineer,6.9,6,47,7,Normal Weight,117/76,69,6800,None
+205,Male,43,Engineer,7.6,8,75,4,Overweight,122/80,68,6800,None
+206,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+207,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+208,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+209,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+210,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+211,Male,43,Engineer,7.7,8,90,5,Normal,130/85,70,8000,None
+212,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+213,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+214,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+215,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+216,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+217,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+218,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,None
+219,Male,43,Engineer,7.8,8,90,5,Normal,130/85,70,8000,Sleep Apnea
+220,Male,43,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,Sleep Apnea
+221,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+222,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+223,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+224,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+225,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+226,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+227,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+228,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+229,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+230,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+231,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+232,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+233,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+234,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+235,Female,44,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+236,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+237,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+238,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+239,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+240,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+241,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+242,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+243,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,Insomnia
+244,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+245,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+246,Female,44,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+247,Male,44,Salesperson,6.3,6,45,7,Overweight,130/85,72,6000,Insomnia
+248,Male,44,Engineer,6.8,7,45,7,Overweight,130/85,78,5000,Insomnia
+249,Male,44,Salesperson,6.4,6,45,7,Overweight,130/85,72,6000,None
+250,Male,44,Salesperson,6.5,6,45,7,Overweight,130/85,72,6000,None
+251,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+252,Female,45,Teacher,6.8,7,30,6,Overweight,135/90,65,6000,Insomnia
+253,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+254,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+255,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+256,Female,45,Teacher,6.5,7,45,4,Overweight,135/90,65,6000,Insomnia
+257,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+258,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+259,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+260,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+261,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,Insomnia
+262,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+263,Female,45,Teacher,6.6,7,45,4,Overweight,135/90,65,6000,None
+264,Female,45,Manager,6.9,7,55,5,Overweight,125/82,75,5500,None
+265,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+266,Female,48,Nurse,5.9,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+267,Male,48,Doctor,7.3,7,65,5,Obese,142/92,83,3500,Insomnia
+268,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,None
+269,Female,49,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+270,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+271,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+272,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+273,Female,49,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+274,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+275,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+276,Female,49,Nurse,6.2,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+277,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+278,Male,49,Doctor,8.1,9,85,3,Obese,139/91,86,3700,Sleep Apnea
+279,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Insomnia
+280,Female,50,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+281,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,None
+282,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+283,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+284,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+285,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+286,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+287,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+288,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+289,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+290,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+291,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+292,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+293,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+294,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+295,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+296,Female,50,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+297,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+298,Female,50,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+299,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+300,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+301,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+302,Female,51,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+303,Female,51,Nurse,7.1,7,55,6,Normal Weight,125/82,72,6000,None
+304,Female,51,Nurse,6,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+305,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+306,Female,51,Nurse,6.1,6,90,8,Overweight,140/95,75,10000,Sleep Apnea
+307,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+308,Female,52,Accountant,6.5,7,45,7,Overweight,130/85,72,6000,Insomnia
+309,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+310,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+311,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+312,Female,52,Accountant,6.6,7,45,7,Overweight,130/85,72,6000,Insomnia
+313,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+314,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+315,Female,52,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+316,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,Insomnia
+317,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+318,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+319,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+320,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+321,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+322,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+323,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+324,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+325,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+326,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+327,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+328,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+329,Female,53,Engineer,8.3,9,30,3,Normal,125/80,65,5000,None
+330,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+331,Female,53,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+332,Female,53,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+333,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+334,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+335,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+336,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+337,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+338,Female,54,Engineer,8.4,9,30,3,Normal,125/80,65,5000,None
+339,Female,54,Engineer,8.5,9,30,3,Normal,125/80,65,5000,None
+340,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+341,Female,55,Nurse,8.1,9,75,4,Overweight,140/95,72,5000,Sleep Apnea
+342,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+343,Female,56,Doctor,8.2,9,90,3,Normal Weight,118/75,65,10000,None
+344,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+345,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+346,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+347,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+348,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+349,Female,57,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+350,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+351,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+352,Female,57,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+353,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+354,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+355,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+356,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+357,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+358,Female,58,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+359,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,None
+360,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,None
+361,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+362,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+363,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+364,Female,59,Nurse,8.2,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+365,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+366,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+367,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+368,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+369,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+370,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+371,Female,59,Nurse,8,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+372,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+373,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea
+374,Female,59,Nurse,8.1,9,75,3,Overweight,140/95,68,7000,Sleep Apnea

sleep_health/best_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62f6879cb46a20e7b43dce5dd42a26b3a5da760fd9635df7799d6d1d42d18bae
+size 137954

sleep_health/label_encoders.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:495d06de358ed8aa738672948af7a91cc556b6d6ab36d4ed77bd26adde9ee44e
+size 882

sleep_health/scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:04a40f6f9e9ea925cb8781771d8fa0bcab70fc81231ce6b9caf607ec75c807d2
+size 2196

sleep_health/svc_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:138df639230fd2987a2dd5b96ad0d181a829b3841c8e67868c6f66bba330333a
+size 27127

stroke/finalized_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df868c250b76e6e13a1af55006426fffec4bb1e0015477824c6852c3a7a49d92
+size 3727075