model_build.ipynb

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    "After playing around some other scripts, I now have some more understandings about the data set as well as some methods which could be apply into the model.\n",
    "\n",
    "The plan is:\n",
    "- Log(1+x) transform training labels (log1p)\n",
    "- Clear variables which are highly correlated with another variables and contains a large number of missing values\n",
    "- Clear the outliers\n",
    "- Create polynomials for top important variables\n",
    "- Log transform the skewed numerical variables\n",
    "- Here may be try to standardize numerical variables\n",
    "- One hot for categorical variables"
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "from scipy.stats import skew\n",
    "from scipy.stats.stats import pearsonr\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression, RidgeCV, LassoCV, ElasticNetCV, ElasticNet\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
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   "source": [
    "# Load data"
   ]
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