House Price Prediction using Advanced Regression Techniques

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

import os
import warnings
import numpy as np 
import pandas as pd 
import pandas_profiling as pp
from sklearn.linear_model import Lasso
from sklearn.linear_model import LassoCV
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder

print(os.listdir("../input/house-prices-advanced-regression-techniques"))
%matplotlib inline

train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test  = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')

def display_all(df):
    with pd.option_context("display.max_rows", 1000, "display.max_columns", 1000): 
        display(df)