Add Streamlit app and files

README.md

@@ -35,6 +35,20 @@ model/vo2_predictor.joblib: The saved machine learning pipeline.
 app/app.py: (Assuming this is your Streamlit app file based on the requirements.txt) The Streamlit application code.
 This notebook: Contains the code for data loading, cleaning, model training, evaluation, saving, and a function for generating coaching tips.
 
+vo2max-readiness-ai/
+├─ app/app.py
+├─ assets/Baseline_Data_Insight1b # Original dataset 
+├─ assets/vo2_real_augmented.csv  # Synthetic dataset 
+├─ model/vo2_predictor.joblib
+├─ src/VO2Max_Data_Wrangler.ipynb
+├─ src/VO2Max_Data_Predictor.ipynb                 # your Colab-exported notebook
+├─ requirements.txt
+└─ README.md
+
+Citation
+Larsen, Ryan; Hillman, Charles; Kramer, Arthur; Cohen, Neal; Barbey, Aron. (2021). Magnetic Resonance Spectroscopy Imaging, Body Composition, and Cardiorespiratory Fitness: Baseline Data from a Multimodal Intervention Study. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9371397_V1
+_]()
+
 Setup and Usage
 Clone the repository:
   cd <project_directory>

Readme.txt

@@ -0,0 +1,111 @@
+This readme.txt file was generated on 2020-03-23 by Ryan J. Larsen
+
+GENERAL INFORMATION
+
+1. Title of Dataset: Magnetic Resonance Spectroscopy Imaging, body composition, and cardiorespiratory fitness: Baseline data from a multimodal intervention study 
+
+2. Author Information
+	A. Principal Investigator Contact Information
+		Name: Aron K. Barbey
+		Institution: University of Illinois at Urbana-Champaign
+		Email: barbey@illinois.edu
+
+	B. Alternate Contact Information
+		Name: Ryan J. Larsen
+		Institution: University of Illinois at Urbana-Champaign
+		Email: larsen@illinois.edu
+
+3. Date of data collection:2015
+
+4. Geographic location of data collection:  Champaign-Urbana, IL, USA
+
+5. Information about funding sources that supported the collection of the data: The research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via Contract 2014-13121700004 to the University of Illinois at Urbana-Champaign (PI: Barbey). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
+
+
+SHARING/ACCESS INFORMATION
+
+1. Licenses/restrictions placed on the data: CC0 1.0 Universal public domain dedication
+
+2. Links to publications that cite or use the data:  "Body mass and cardiorespiratory fitness are associated with altered brain metabolism." Ryan J. Larsen, Lauren B. Raine, Charles H. Hillman, Arthur F. Kramer, Neal J. Cohen, Aron K. Barbey.  Metabolic Brain Disease, 2020.
+
+5. Was data derived from another source? no
+
+6. Recommended citation for this dataset: Larsen, Ryan; Charles, Hillman; Kramer, Arthur; Cohen, Neal; Barbey, Aron (2021) Magnetic Resonance Spectroscopy Imaging, body composition, and cardiorespiratory fitness: Baseline data from a multimodal intervention study . University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9371397_V1
+
+
+DATA & FILE OVERVIEW
+
+1. File List: 
+Baseline_Data_Insight1b.csv:
+Speadsheet of measures from Magnetic Resonance Spectroscopy Imaging, body composition, and cardiorespiratory fitness
+
+2. Are there multiple versions of the dataset? no
+
+
+METHODOLOGICAL INFORMATION
+
+1. Description of methods used for collection/generation of data: 
+ See "Body mass and cardiorespiratory fitness are associated with altered brain metabolism." Ryan J. Larsen, Lauren B. Raine, Charles H. Hillman, Arthur F. Kramer, Neal J. Cohen, Aron K. Barbey.  Metabolic Brain Disease, 2020.
+
+2. Methods for processing the data: 
+
+For a description of analysis performed using this data set, see "Body mass and cardiorespiratory fitness are associated with altered brain metabolism." Ryan J. Larsen, Lauren B. Raine, Charles H. Hillman, Arthur F. Kramer, Neal J. Cohen, Aron K. Barbey.  Metabolic Brain Disease, 2020.
+
+For a more detailed description of the method used to process the MRSI data, see
+Larsen RJ, Newman M, Nikolaidis A (2016) Reduction of variance in measurements of average metabolite concentration in anatomically-defined brain regions J Magn Reson 272:73-81 doi:10.1016/j.jmr.2016.09.005
+
+3. Describe any quality-assurance procedures performed on the data:
+For the following subjects, height data have been discarded due to inconsistency with post-intervention height measurement: 1297,1314,1350
+For analysis in the cited article, when Confidence<5, VO2max data were discarded
+
+4. People involved with sample collection, processing, analysis and/or submission: 
+Ryan J. Larsen
+
+
+DATA-SPECIFIC INFORMATION FOR: Baseline_Data_Insight1b
+
+1. Number of variables: 45
+
+2. Number of cases/rows: 435
+
+3. Variable List: 
+
+Subject Number, arbitrary identifier
+Age, units: years
+Sex
+Height, body height, units: cm
+Mass, body mass, units: kg
+Total_Lean_Mass, total lean body mass, units: kg
+BMI, body mass index, units: kg/m^2
+WBTPF, Whole Body Total Percent Fat
+VO2max_abs, VO2max_absolute, units: Liters/min
+VO2max_rel, VO2max_relative, units: mL/kg/min
+Confidence, a number indicating the degree of confidence in the accuracy of the measurement, a higher number indicates greater confidence   
+Fat_Free_VO2max, Fat Free VO2max, VO2max_absolute divided by total lean body mass
+
+MRS data
+VOI_RL, the Volume of Interest in the Right-Left direction
+VOI_AP, the Volume of Interest in the Anterior-Posterior direction
+Tech, the technician who performed the scan
+
+Five variables are given for each brain region:
+
+tNAA_Cr is NAA/Cr
+GM is gray matter fraction
+WM is white matter fraction
+CSF is cerebral spinal fluid fraction
+scanfrac is the volume fraction of the region within the Volume of Interest 
+
+Brain regions are indicated by appending the following indicators to variable names:
+
+Ant_Right is right anterior gray matter
+Ant_Left is left anterior gray matter  
+Pstr_Right is right posterior gray matter
+Pstr_Left is left posterior gray matter
+WM_Right is right white matter
+WM_Left is left white matter
+
+4. Missing data codes: 
+Missing numbers are coded as NaN
+Missing categorical variables are coded as <undefined>
+

app/app.py

@@ -0,0 +1,104 @@
+import streamlit as st
+import pandas as pd, joblib, json
+
+st.set_page_config(page_title="VO₂ Max & Training Readiness", page_icon="🏃", layout="centered")
+st.title("🏃 VO₂ Max & Training Readiness (Synthetic, Demo)")
+st.caption("CPU-only • Synthetic data • Not medical advice.")
+
+MODEL_PATH = "model/vo2_predictor.joblib"
+DATA_PATH = "assets/vo2max_synthetic.csv"
+
+@st.cache_resource
+def load_model():
+    return joblib.load(MODEL_PATH)
+
+@st.cache_data
+def load_sample():
+    try:
+        df = pd.read_csv(DATA_PATH)
+        return df
+    except Exception:
+        return pd.DataFrame()
+
+pipe = load_model()
+df = load_sample()
+
+with st.expander("Sample data (first 50 rows)"):
+    if not df.empty:
+        st.dataframe(df.head(50), use_container_width=True)
+    else:
+        st.info("Sample CSV not found.")
+
+st.subheader("Enter runner metrics")
+cols = st.columns(2)
+
+sex = cols[0].selectbox("Sex (0=female, 1=male)", [0,1], index=1)
+age = cols[1].slider("Age", 18, 70, 35)
+height_cm = cols[0].slider("Height (cm)", 150, 200, 172)
+weight_kg = cols[1].slider("Weight (kg)", 45, 120, 74)
+
+resting_hr = cols[0].slider("Resting HR (bpm)", 40, 100, 60)
+max_hr = cols[1].slider("Max HR (bpm)", 150, 205, 185)
+avg_hr_during_run = cols[0].slider("Avg HR During Run (bpm)", 95, 190, 150)
+hr_recovery_1min = cols[1].slider("HR Recovery 1 min (bpm drop)", 10, 55, 30)
+
+distance_km = cols[0].slider("Distance of last run (km)", 1, 30, 5)
+duration_min = cols[1].slider("Duration of last run (min)", 10, 180, 30)
+pace_min_per_km = max(3.5, min(12.0, duration_min / max(distance_km, 0.5)))
+avg_speed_kmh = 60.0 / pace_min_per_km
+elevation_gain_m = cols[0].slider("Elevation Gain (m)", 0, 600, 50)
+
+training_hours_week = cols[1].slider("Training Hours / Week", 0, 12, 4)
+avg_intensity = cols[0].slider("Avg Intensity (1–10)", 1, 10, 6)
+rest_days = cols[1].slider("Rest Days (last 7d)", 0, 4, 1)
+
+sleep_hours_last_night = cols[0].slider("Sleep Hours Last Night", 3, 10, 7)
+avg_sleep_hours_week = cols[1].slider("Avg Sleep Hours / Week", 4, 9, 7)
+sleep_quality_score = cols[0].slider("Sleep Quality (0–100)", 25, 95, 72)
+resting_hr_delta = resting_hr - 60
+
+temperature_C = cols[1].slider("Temperature (°C)", 0, 35, 18)
+humidity_pct = cols[0].slider("Humidity (%)", 15, 95, 55)
+altitude_m = cols[1].slider("Altitude (m)", 0, 2200, 200)
+
+hr_ratio = avg_hr_during_run / max_hr if max_hr>0 else 0
+training_load_index = distance_km * (avg_hr_during_run/100.0) / (duration_min/60.0 + 0.1)
+speed_per_kg = avg_speed_kmh / (weight_kg/70.0)
+
+features = {
+ "sex":sex,"age":age,"height_cm":height_cm,"weight_kg":weight_kg,
+ "bmi": weight_kg/((height_cm/100.0)**2),
+ "resting_hr":resting_hr,"max_hr":max_hr,"avg_hr_during_run":avg_hr_during_run,
+ "hr_recovery_1min":hr_recovery_1min,
+ "distance_km":distance_km,"duration_min":duration_min,
+ "pace_min_per_km":pace_min_per_km,"avg_speed_kmh":avg_speed_kmh,
+ "elevation_gain_m":elevation_gain_m,
+ "training_hours_week":training_hours_week,"avg_intensity":avg_intensity,"rest_days":rest_days,
+ "sleep_hours_last_night":sleep_hours_last_night,"avg_sleep_hours_week":avg_sleep_hours_week,
+ "sleep_quality_score":sleep_quality_score,"resting_hr_delta":resting_hr_delta,
+ "temperature_C":temperature_C,"humidity_pct":humidity_pct,"altitude_m":altitude_m,
+ "hr_ratio":hr_ratio,"training_load_index":training_load_index,"speed_per_kg":speed_per_kg
+}
+
+def coaching_tip(vo2, sleep_quality, training_hours, hrr):
+    tips = []
+    if vo2 < 38: tips.append("Prioritize easy aerobic runs (Zone 2) 3–4x/week.")
+    elif vo2 < 48: tips.append("Add 1 weekly interval (e.g., 4×4min hard, full recovery).")
+    else: tips.append("Maintain with polarized training; 80% easy, 20% hard.")
+
+    if sleep_quality < 60: tips.append("Boost sleep hygiene to 7–8h; reduce late caffeine/screens.")
+    if training_hours > 8: tips.append("High load detected — deload 10–20% this week.")
+    if hrr < 20: tips.append("Low HRR — keep intensities sub-threshold for 2–3 days.")
+    return " ".join(tips) or "Keep up the good work!"
+
+if st.button("Predict VO₂ max"):
+    x = pd.DataFrame([features])
+    pipe = load_model()
+    vo2 = float(pipe.predict(x)[0])
+    tip = coaching_tip(vo2, sleep_quality_score, training_hours_week, hr_recovery_1min)
+
+    st.metric("Estimated VO₂ max (ml/kg/min)", f"{vo2:.1f}")
+    st.success(tip)
+
+st.divider()
+st.caption("Limitations: Synthetic data; not a medical device; demo use only.")

assets/vo2_real_augmented.csv

@@ -0,0 +1,425 @@
+age,sex,height_cm,weight_kg,total_lean_mass,bmi,wbtpf,vo2max,resting_hr,max_hr,hr_recovery_1min,training_hours_week,avg_intensity,rest_days,sleep_hours_last_night,avg_sleep_hours_week,sleep_quality_score,resting_hr_delta
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+21.2,0,163.4,56.4,36.1424574149003,21.1239436155502,36.7946555872524,29.4,57.02467379224487,201.31303218722115,27.13863337191478,3.4109137098495994,4,0,7.910684339408207,7.462365229344771,65.90637672790332,-3.4468085056745537

model/vo2_predictor.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:69949edb3e8c971968ceadd35f82e838957b9b96627d2d2c2ebbbbc67e817a7a
+size 6748202

src/VO2Max_Data_Wrangler.ipynb

@@ -0,0 +1,232 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "v79as6XRiDqE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "daba9903"
+      },
+      "source": [
+        "# VO2 Max Prediction\n",
+        "\n",
+        "This notebook demonstrates the process of predicting VO2 max using a dataset containing various physiological and training-related features.\n",
+        "\n",
+        "**Steps:**\n",
+        "\n",
+        "1.  **Setup and Imports**: Install necessary libraries and import modules.\n",
+        "2.  **Import and Inspect Dataset**: Load and explore the initial dataset.\n",
+        "3.  **Clean and Prepare Data**: Preprocess the data for analysis.\n",
+        "4.  **Augment with Synthetic Features**: Add simulated features to enrich the dataset.\n",
+        "5.  **Export Augmented Data Set**: Export augmented dataset for use in training pipeline.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Setup and Imports"
+      ],
+      "metadata": {
+        "id": "8pnzsYhMmn5z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "czA2E20LIfpT"
+      },
+      "outputs": [],
+      "source": [
+        "!pip -q install numpy pandas scikit-learn streamlit joblib matplotlib\n",
+        "\n",
+        "import numpy as np, pandas as pd\n",
+        "import os\n",
+        "from numpy.random import default_rng\n",
+        "rng = default_rng(42)\n",
+        "\n",
+        "import os, json, joblib, math"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Import and Inspect Dataset"
+      ],
+      "metadata": {
+        "id": "Kkq_ss4qOwQk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!ls -lh /content\n",
+        "df = pd.read_csv(\"/content/sample_data/SPO2Max dataset/Baseline_Data_Insight1b.csv\")\n",
+        "df.head(), df.info(), df.shape"
+      ],
+      "metadata": {
+        "id": "7_eXmAotIxF5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Clean and Prepare Data"
+      ],
+      "metadata": {
+        "id": "qy_LxfnvOeLD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.columns = df.columns.str.strip().str.replace(\" \", \"_\").str.lower()\n",
+        "\n",
+        "# select relevant fields\n",
+        "cols = [\"age\",\"sex\",\"height\",\"mass\",\"total_lean_mass\",\"bmi\",\"wbtpf\",\"vo2max_rel\"]\n",
+        "df = df[cols].dropna()\n",
+        "\n",
+        "# rename for consistency with earlier runbook\n",
+        "df = df.rename(columns={\n",
+        "    \"mass\": \"weight_kg\",\n",
+        "    \"height\": \"height_cm\",\n",
+        "    \"vo2max_rel\": \"vo2max\"\n",
+        "})\n",
+        "\n",
+        "# encode sex\n",
+        "if df[\"sex\"].dtype == \"object\":\n",
+        "    df[\"sex\"] = df[\"sex\"].str.upper().map({\"M\": 1, \"F\": 0})\n",
+        "\n",
+        "df.head(), df.shape\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5IGi3v-FNzsH",
+        "outputId": "7666ed41-1ee2-4590-f272-c779cbc134e8"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(    age  sex  height_cm  weight_kg  total_lean_mass        bmi      wbtpf  \\\n",
+              " 0  24.2    1      174.0      103.9        64.940172  34.317611  38.403303   \n",
+              " 1  29.6    0      169.0       64.3        45.163956  22.513217  29.592141   \n",
+              " 2  32.9    1      188.4      115.2        79.401800  32.455678  32.061826   \n",
+              " 3  26.7    0      161.1       83.3        47.708081  32.096222  43.168116   \n",
+              " 4  22.5    0      162.8       61.8        45.926730  23.317376  26.905369   \n",
+              " \n",
+              "    vo2max  \n",
+              " 0    25.3  \n",
+              " 1    36.6  \n",
+              " 2    35.8  \n",
+              " 3    30.4  \n",
+              " 4    56.1  ,\n",
+              " (424, 8))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Augment with synthetic features\n",
+        "\n",
+        "Since this real data lacks sleep, HR, training, we are simulating those. We will merge these synthetic features on top so the model can train with richer input. For roadmap, we will collect sleep, HR and training data from wearables"
+      ],
+      "metadata": {
+        "id": "Pup47npPPecI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "N = len(df)\n",
+        "rng = np.random.default_rng(123)\n",
+        "\n",
+        "# synthetic HR / recovery\n",
+        "df[\"resting_hr\"] = (rng.normal(60, 6, N) + 0.1*(df[\"bmi\"] - 24)).clip(45, 95)\n",
+        "df[\"max_hr\"] = (220 - df[\"age\"]) + rng.normal(0, 3, N)\n",
+        "df[\"hr_recovery_1min\"] = (30 + rng.normal(0, 4, N) - 0.05*(df[\"age\"] - 30)).clip(10, 55)\n",
+        "\n",
+        "# synthetic training load\n",
+        "df[\"training_hours_week\"] = rng.normal(4, 2, N).clip(0, 10)\n",
+        "df[\"avg_intensity\"] = rng.integers(3, 9, N)\n",
+        "df[\"rest_days\"] = rng.integers(0, 3, N)\n",
+        "\n",
+        "# synthetic sleep / recovery features\n",
+        "df[\"sleep_hours_last_night\"] = rng.normal(7, 1, N).clip(4, 9)\n",
+        "df[\"avg_sleep_hours_week\"] = (df[\"sleep_hours_last_night\"] + rng.normal(0, 0.7, N)).clip(5, 8)\n",
+        "df[\"sleep_quality_score\"] = (rng.normal(70, 10, N)\n",
+        "                             - 0.3*(df[\"resting_hr\"] - 60)).clip(30, 95)\n",
+        "df[\"resting_hr_delta\"] = df[\"resting_hr\"] - df[\"resting_hr\"].median()"
+      ],
+      "metadata": {
+        "id": "MbgTjrVWPETF"
+      },
+      "execution_count": 13,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Export Augmeted Data Set"
+      ],
+      "metadata": {
+        "id": "FgKXsPQkm3gK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "os.makedirs(\"assets\", exist_ok=True)\n",
+        "df.to_csv(\"assets/vo2_real_augmented.csv\", index=False)\n",
+        "print(\"Saved → assets/vo2_real_augmented.csv\", \"rows:\", len(df))"
+      ],
+      "metadata": {
+        "id": "YaOLkxkaRR-b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b2594606-453a-4786-938c-737d5b9769f7"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved → assets/vo2_real_augmented.csv rows: 424\n"
+          ]
+        }
+      ]
+    }
+  ]
+}

src/VO2Max_Predictor.ipynb

@@ -0,0 +1,369 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e68654b9"
+      },
+      "source": [
+        "# VO2 Max Prediction\n",
+        "\n",
+        "This notebook demonstrates the process of predicting VO2 max using a dataset containing various physiological and training-related features.\n",
+        "\n",
+        "**Steps:**\n",
+        "\n",
+        "1.  **Setup and Imports**: Install necessary libraries and import modules.\n",
+        "2.  **Import and Inspect Dataset**: Load and explore the initial dataset.\n",
+        "3.  **Clean and Prepare Data**: Preprocess the data for analysis.\n",
+        "4.  **Model Training**: Train a machine learning model to predict VO2 max.\n",
+        "6.  **Model Evaluation**: Evaluate the performance of the trained model.\n",
+        "7.  **Inference**: Use the trained model to make predictions.\n",
+        "8.  **Sleep v/s VO2 Max Correlation**: Inspect the training data and plot correlation between Sleep and VO2 Max."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "czA2E20LIfpT"
+      },
+      "outputs": [],
+      "source": [
+        "!pip -q install numpy pandas scikit-learn streamlit joblib matplotlib\n",
+        "\n",
+        "import pandas as pd, numpy as np, joblib\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.metrics import mean_absolute_error, r2_score"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Import and Inspect Dataset"
+      ],
+      "metadata": {
+        "id": "Kkq_ss4qOwQk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv(\"assets/vo2_real_augmented.csv\")\n",
+        "df.head(), df.info(), df.shape"
+      ],
+      "metadata": {
+        "id": "7_eXmAotIxF5"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Clean and Prepare Data"
+      ],
+      "metadata": {
+        "id": "qy_LxfnvOeLD"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.columns = df.columns.str.strip().str.replace(\" \", \"_\").str.lower()\n",
+        "\n",
+        "# select relevant fields\n",
+        "cols = [\"age\",\"sex\",\"height_cm\",\"weight_kg\",\"total_lean_mass\",\"bmi\",\"wbtpf\",\"vo2max\",\"resting_hr\",\"max_hr\",\"hr_recovery_1min\",\"training_hours_week\",\"avg_intensity\",\"rest_days\",\"sleep_hours_last_night\",\"avg_sleep_hours_week\",\"sleep_quality_score\",\"resting_hr_delta\"]\n",
+        "df = df[cols].dropna()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5IGi3v-FNzsH"
+      },
+      "execution_count": 38,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Model Training"
+      ],
+      "metadata": {
+        "id": "F0Hh4lBUkJQE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "target = \"vo2max\"\n",
+        "features = [c for c in df.columns if c != target]\n",
+        "X_train, X_test, y_train, y_test = train_test_split(df[features], df[target],\n",
+        "                                                    test_size=0.2, random_state=42)"
+      ],
+      "metadata": {
+        "id": "W094k-AjWBHr"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pre = ColumnTransformer([(\"num\", StandardScaler(), features)], remainder=\"drop\")\n",
+        "model = RandomForestRegressor(n_estimators=300, max_depth=10, random_state=42)\n",
+        "pipe = Pipeline([(\"pre\", pre), (\"rf\", model)])\n",
+        "pipe.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "id": "N1t3bw5IWDvK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Model Evaluation"
+      ],
+      "metadata": {
+        "id": "gnK50weYkWRK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "preds = pipe.predict(X_test)\n",
+        "print(f\"MAE: {mean_absolute_error(y_test, preds):.2f}, R²: {r2_score(y_test, preds):.3f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TJbLbs8TWH0t",
+        "outputId": "d5fcdbeb-08b3-4c97-fe79-21379517668e"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "MAE: 4.13, R²: 0.603\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Model Export"
+      ],
+      "metadata": {
+        "id": "RbA8ogCxkhjb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "os.makedirs(\"model\", exist_ok=True)\n",
+        "joblib.dump(pipe, \"model/vo2_predictor.joblib\")\n",
+        "print(\"Saved → model/vo2_predictor.joblib\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W-N9pdQFWK1R",
+        "outputId": "f7668838-dcf8-4e66-a02d-0d6fcac31491"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved → model/vo2_predictor.joblib\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Inference"
+      ],
+      "metadata": {
+        "id": "st0_DcDTkmnV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = df.sample(1, random_state=3).to_dict(orient=\"records\")[0]\n",
+        "x = pd.DataFrame([sample])[features]\n",
+        "vo2_pred = pipe.predict(x)[0]\n",
+        "print(\"Predicted VO₂max:\", round(vo2_pred,2), \"ml/kg/min\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YaOLkxkaRR-b",
+        "outputId": "808f4302-fc0c-4d78-b285-7b1590dffbe2"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted VO₂max: 35.03 ml/kg/min\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def coaching_tip(vo2, sleep_quality, training_hours, hrr):\n",
+        "    tips = []\n",
+        "    if vo2 < 38:\n",
+        "        tips.append(\"Focus on easy aerobic runs (Zone 2) 3–4x/week.\")\n",
+        "    elif vo2 < 48:\n",
+        "        tips.append(\"Add 1 interval session (e.g., 4×4min @ hard) each week.\")\n",
+        "    else:\n",
+        "        tips.append(\"Maintain with polarized training (80/20 easy-to-hard).\")\n",
+        "\n",
+        "    if sleep_quality < 60:\n",
+        "        tips.append(\"Prioritize sleep hygiene (7–8h); avoid late caffeine.\")\n",
+        "    if training_hours > 8:\n",
+        "        tips.append(\"Reduce volume 10–20% for recovery this week.\")\n",
+        "    if hrr < 20:\n",
+        "        tips.append(\"Your 1-min HR recovery is low; keep intensities sub-threshold for 2–3 days.\")\n",
+        "\n",
+        "    return \" \".join(tips) or \"Keep up the good work!\"\n",
+        "\n",
+        "# quick smoke test\n",
+        "print(coaching_tip(45, 55, 6, 18))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ogsIGVtVW8Fz",
+        "outputId": "e77d1664-78b4-4ea7-daad-9408caaed866"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Add 1 interval session (e.g., 4×4min @ hard) each week. Prioritize sleep hygiene (7–8h); avoid late caffeine. Your 1-min HR recovery is low; keep intensities sub-threshold for 2–3 days.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "feature_order = features  # keep consistent\n",
+        "\n",
+        "def predict_vo2(sample_dict):\n",
+        "    x = pd.DataFrame([sample_dict])[feature_order]\n",
+        "    vo2 = float(pipe.predict(x)[0])\n",
+        "    tip = coaching_tip(\n",
+        "        vo2=vo2,\n",
+        "        sleep_quality=float(sample_dict[\"sleep_quality_score\"]),\n",
+        "        training_hours=float(sample_dict[\"training_hours_week\"]),\n",
+        "        hrr=float(sample_dict[\"hr_recovery_1min\"]),\n",
+        "    )\n",
+        "    return round(vo2, 2), tip\n",
+        "\n",
+        "sample = df.sample(1, random_state=7).to_dict(orient=\"records\")[0]\n",
+        "sample_vo2, sample_tip = predict_vo2(sample)\n",
+        "sample_vo2, sample_tip"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWzYwu5fXC6W",
+        "outputId": "8ca89225-2cdc-47f5-a45c-b64391410506"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(39.57, 'Add 1 interval session (e.g., 4×4min @ hard) each week.')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Sleep v/s VO2 Max Correlation"
+      ],
+      "metadata": {
+        "id": "Pne6C9-8l3dw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.figure(figsize=(5,4))\n",
+        "plt.scatter(df[\"avg_sleep_hours_week\"], df[\"vo2max\"], alpha=0.3)\n",
+        "plt.xlabel(\"Avg Sleep Hours (week)\")\n",
+        "plt.ylabel(\"VO₂ max (ml/kg/min)\")\n",
+        "plt.title(\"Sleep vs VO₂ max (synthetic)\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "IQGFYZcfXHBb",
+        "outputId": "05530287-6e63-47fb-8337-e3d995e009f9"
+      },
+      "execution_count": 46,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 500x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}