Ridge regression

Big_mart.py

# imputing missing values

train['Item_Visibility'] = train['Item_Visibility'].replace(0,np.mean(train['Item_Visibility']))

train['Outlet_Establishment_Year'] = 2013 - train['Outlet_Establishment_Year']

train['Outlet_Size'].fillna('Small',inplace=True)

# creating dummy variables to convert categorical into numeric values

mylist = list(train1.select_dtypes(include=['object']).columns)

dummies = pd.get_dummies(train[mylist], prefix= mylist)

train.drop(mylist, axis=1, inplace = True)

X = pd.concat([train,dummies], axis =1 )

import numpy as np

import pandas as pd

from pandas import Series, DataFrame

import matplotlib.pyplot as plt

%matplotlib inline

train = pd.read_csv('training.csv')

test = pd.read_csv('testing.csv')

# importing linear regression

from sklearn from sklearn.linear_model import LinearRegression

lreg = LinearRegression()

# for cross validation

from sklearn.model_selection import train_test_split

X = train.drop('Item_Outlet_Sales',1)

x_train, x_cv, y_train, y_cv = train_test_split(X,train.Item_Outlet_Sales, test_size =0.3)

# training a linear regression model on train

lreg.fit(x_train,y_train)

# predicting on cv

pred_cv = lreg.predict(x_cv)

# calculating mse

mse = np.mean((pred_cv - y_cv)**2)

mse


# evaluation using r-square

lreg.score(x_cv,y_cv)





from sklearn.linear_model import Ridge

## training the model

ridgeReg = Ridge(alpha=0.05, normalize=True)

ridgeReg.fit(x_train,y_train)

pred = ridgeReg.predict(x_cv)

calculating mse

mse = np.mean((pred_cv - y_cv)**2)

mse  ## calculating score ridgeReg.score(x_cv,y_cv) 0.5691