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   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\3575846689.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n",
      "C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\3575846689.py:9: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1518/1518 [==============================] - 2s 758us/step - loss: 0.3609 - accuracy: 0.9112\n",
      "Epoch 2/10\n",
      "1518/1518 [==============================] - 1s 726us/step - loss: 0.2763 - accuracy: 0.9216\n",
      "Epoch 3/10\n",
      "1518/1518 [==============================] - 1s 731us/step - loss: 0.2751 - accuracy: 0.9216\n",
      "Epoch 4/10\n",
      "1518/1518 [==============================] - 1s 725us/step - loss: 0.2750 - accuracy: 0.9216\n",
      "Epoch 5/10\n",
      "1518/1518 [==============================] - 1s 733us/step - loss: 0.2750 - accuracy: 0.9216\n",
      "Epoch 6/10\n",
      "1518/1518 [==============================] - 1s 733us/step - loss: 0.2750 - accuracy: 0.9216\n",
      "Epoch 7/10\n",
      "1518/1518 [==============================] - 1s 729us/step - loss: 0.2750 - accuracy: 0.9216\n",
      "Epoch 8/10\n",
      "1518/1518 [==============================] - 1s 728us/step - loss: 0.2750 - accuracy: 0.9216\n",
      "Epoch 9/10\n",
      "1518/1518 [==============================] - 1s 727us/step - loss: 0.2750 - accuracy: 0.9216\n",
      "Epoch 10/10\n",
      "1518/1518 [==============================] - 1s 755us/step - loss: 0.2750 - accuracy: 0.9216\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "\n",
    "train_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')\n",
    "\n",
    "X_train = train_data[['Sex']]\n",
    "y_train = train_data['Medal']\n",
    "\n",
    "X_train.loc[:, 'Sex'] = X_train['Sex'].map({'M': 0, 'F': 1})\n",
    "y_train = y_train.map({'Bronze': 0, 'Silver': 1, 'Gold': 1}).fillna(0).astype('float32')\n",
    "\n",
    "X_train = X_train.astype('float32')\n",
    "y_train = y_train.astype('float32')\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Dense(16, activation='relu', input_shape=(X_train.shape[1],)),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train, epochs=10)\n",
    "\n",
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 170/1518 [==>...........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kmjay\\AppData\\Local\\Temp\\ipykernel_17164\\2746302769.py:3: DeprecationWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`\n",
      "  test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1518/1518 [==============================] - 1s 574us/step\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('olympics-124-years-datasettill-2020/Athletes_winter_games.csv')\n",
    "\n",
    "test_data.loc[:, 'Sex'] = test_data['Sex'].map({'M': 0, 'F': 1})\n",
    "test_data = test_data[['Sex']].astype('float32')\n",
    "\n",
    "predictions = model.predict(test_data)\n",
    "\n",
    "pd.DataFrame(predictions).to_csv('predictions.csv', index=False, header=False)"
   ]
  }
 ],
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