reg = RandomForestRegressor()
reg.fit(Xtrain, ytrain)
preds = reg.predict(Xval)
print(
f'The validation RMSLE error for baseline model is: {RMSLE(np.exp(yval), np.exp(preds))}'
)
sub_preds = reg.predict(test[cols])
submit['units_sold'] = np.exp(sub_preds)
submit.head(2)
submit.to_csv('sub_baseline_v1.csv', index=False)
from category_encoders import TargetEncoder, MEstimateEncoder
encoder = MEstimateEncoder()
encoder.fit(train['store_id'], train['units_sold'])
train['store_encoded'] = encoder.transform(train['store_id'],
train['units_sold'])
test['store_encoded'] = encoder.transform(test['store_id'], test['units_sold'])
encoder.fit(train['sku_id'], train['units_sold'])
train['sku_encoded'] = encoder.transform(train['sku_id'], train['units_sold'])
test['sku_encoded'] = encoder.transform(test['sku_id'], test['units_sold'])
skus = train.sku_id.unique()
print(skus[:2])
test_preds = test.copy()
test_preds.tail(2)
def sku_model(sku, cols_to_use, reg):
X_encode = df.sample(frac=0.20, random_state=0)
y_encode = X_encode.pop("SalePrice")
# Training split
X_pretrain = df.drop(X_encode.index)
y_train = X_pretrain.pop("SalePrice")
# YOUR CODE HERE: Create the MEstimateEncoder
# Choose a set of features to encode and a value for m
encoder = MEstimateEncoder(
cols=["Neighborhood"],
m=1.0,
)
# Fit the encoder on the encoding split
encoder.fit(X_encode, y_encode)
# Encode the training split
X_train = encoder.transform(X_pretrain, y_train)
feature = encoder.cols
plt.figure(dpi=90)
ax = sns.distplot(y_train, kde=True, hist=False)
ax = sns.distplot(X_train[feature], color='r', ax=ax, hist=True, kde=False, norm_hist=True)
ax.set_xlabel("SalePrice");
X = df.copy()
y = X.pop("SalePrice")
score_base = score_dataset(X, y)
score_new = score_dataset(X_train, y_train)
rcParams['figure.figsize']=10,6
df1=np.log1p(df['UnitPrice'])
df.drop("UnitPrice", axis = 1, inplace = True)
!pip install category_encoders
from category_encoders import MEstimateEncoder
df=df.astype({'StockCode': 'category','Description': 'category'})
category_list=['StockCode','Description']
encoder_final=MEstimateEncoder()
encoder_final.fit(df[category_list], df1)
cat_enc = encoder_final.transform(df[category_list], df1)
continuous_train = df.drop(columns= category_list)
df = pd.concat([cat_enc,continuous_train],axis=1)
test_enc=encoder_final.transform(df2[category_list])
continuous_test=df2.drop(columns= category_list)
df2=pd.concat([test_enc,continuous_test],axis=1)
df2.head()
sns.distplot(np.log1p(df['Quantity']))
from scipy import stats
y=np.log1p(df['UnitPrice'])