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Update Bike_Price_Prediction #24

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6b968cb
Update Bike_Price_Prediction
ShivamGoyal49 Oct 20, 2022
b393bd7
Update Laptop Price Prediction
ShivamGoyal49 Oct 20, 2022
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18 changes: 18 additions & 0 deletions Bike_Price_Prediction
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Original file line number Diff line number Diff line change
Expand Up @@ -98,3 +98,21 @@ y_pred = pipe.predict(X_test)

print('R2 score',r2_score(y_test,y_pred))
print('MAE',mean_absolute_error(y_test,y_pred))

step1 = ColumnTransformer(transformers=[
('col_tnf',OneHotEncoder(sparse=False,drop='first'),[1,2,5])
],remainder='passthrough')

step2 = Ridge(alpha=10)

pipe = Pipeline([
('step1',step1),
('step2',step2)
])

pipe.fit(X_train,y_train)

y_pred = pipe.predict(X_test)

print('R2 score',r2_score(y_test,y_pred))
print('MAE',mean_absolute_error(y_test,y_pred))
18 changes: 18 additions & 0 deletions Laptop Price Prediction
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,24 @@ plt.show()
sns.barplot(x=df['TypeName'],y=df['Price'])
plt.xticks(rotation='vertical')
plt.show()

step1 = ColumnTransformer(transformers=[
('col_tnf',OneHotEncoder(sparse=False,drop='first'),[0,1,7,10,11])
],remainder='passthrough')

step2 = Ridge(alpha=10)

pipe = Pipeline([
('step1',step1),
('step2',step2)
])

pipe.fit(X_train,y_train)

y_pred = pipe.predict(X_test)

print('R2 score',r2_score(y_test,y_pred))
print('MAE',mean_absolute_error(y_test,y_pred))
# Workstation type laptops are most expensive
# laptop price also depends upon on type of laptop

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