Coupon Purchase Prediction - BTB Script.ipynb

Coupon Purchase Prediction - First Script.ipynb

Flavor of Physics - Classification.ipynb

Flavor of Physics - Mix of models.ipynb

Springleaf - Experiments with Classification.ipynb

Springleaf - Experiments with Random Forest.ipynb

SpringLeaf -Kaggle 18AUG15 (1).ipynb

SpringLeaf -Kaggle 18AUG15.ipynb

Springleaf with xgb.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from datetime import  datetime\n",
    "\n",
    "\n",
    "def get_data():\n",
    "    train = pd.read_csv(\"../input/train.csv\")\n",
    "    test = pd.read_csv(\"../input/test.csv\")\n",
    "\n",
    "    features = train.select_dtypes(include=['float']).columns\n",
    "    features = np.setdiff1d(features,['ID','target'])\n",
    "\n",
    "    test_ids = test.ID\n",
    "    y_train = train.target\n",
    "\n",
    "    x_train = train[features]\n",
    "    x_test = test[features]\n",
    "\n",
    "    return x_train, y_train, x_test, test_ids\n",
    "\n",
    "ts = datetime.now()\n",
    "x_train, y_train, x_test, test_ids = get_data()\n",
    "\n",
    "\n",
    "xgb_params = {\"objective\": \"binary:logistic\", \"max_depth\": 10, \"silent\": 1}\n",
    "num_rounds = 200\n",
    "\n",
    "dtrain = xgb.DMatrix(x_train, label=y_train)\n",
    "dtest = xgb.DMatrix(x_test)\n",
    "gbdt = xgb.train(xgb_params, dtrain, num_rounds)\n",
    "\n",
    "preds = gbdt.predict(dtest)\n",
    "\n",
    "\n",
    "submission = pd.DataFrame({\"ID\": test_ids, \"target\": preds})\n",
    "submission = submission.set_index('ID')\n",
    "submission.to_csv('xgb_benchmark.csv')\n",
    "\n",
    "te = datetime.now()\n",
    "print('elapsed time: {0}'.format(te-ts))"
   ]
  }
 ],
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Springleaf with xgb1.ipynb