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   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "# Wczytywanie danych\n",
    "data = pd.read_csv('openpowerlifting.csv')\n",
    "\n",
    "# Zakładając, że kolumny to 'squat', 'bench_press', 'deadlift' i 'total'\n",
    "features = data[['squat', 'bench_press', 'deadlift']]\n",
    "target = data['total']\n",
    "\n",
    "# Podział na dane treningowe i testowe\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)\n",
    "\n",
    "# Normalizacja danych\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)  # Używamy tego samego scaler do danych testowych\n",
    "\n",
    "# Tworzenie modelu\n",
    "model = Sequential([\n",
    "    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
    "    Dense(64, activation='relu'),\n",
    "    Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse', metrics=['mae'])\n",
    "\n",
    "# Trenowanie modelu\n",
    "model.fit(X_train, y_train, epochs=10, validation_split=0.1)  # Używam validation_split zamiast oddzielnego zbioru\n",
    "\n",
    "# Save the model\n",
    "model.save('powerlifting_model.h5')\n"
   ]
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