import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import cross_validation
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
data=pd.read_csv('train.csv')
data.head()
df.GarageCond=le.fit_transform(df.GarageCond)
df.GarageYrBlt=le.fit_transform(df.GarageYrBlt)
df.GarageFinish=le.fit_transform(df.GarageFinish)
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42)
model=LogisticRegression()
model.fit(x_train,y_train)
new_prediction=model.predict(testing_data)
from sklearn.metrics import mean_squared_error
rmse = np.sqrt(mean_squared_error(prediction, y_test))
rmse