In this ML Algorithms course tutorial, we are going to learn “Root Mean Square Error in detail. we covered it by practically and theoretical intuition.
- What is an error?
- What is mean square error (MSE)?
- What is Root Mean Square Error (RMSE)?
- What is the cost function?
- Why need to use it?
- How to implement RMSE in python?
# Business Problem - Predict the Price of Bangalore House
#Using Linear Regression - Supervised Machine Learning Algorithm
### Load Libraries
import pandas as pd
"""### Load Data"""
path = r"https://drive.google.com/uc?export=download&id=1xxDtrZKfuWQfl-6KA9XEd_eatitNPnkB"
df = pd.read_csv(path)
df.head()
"""### Split Data"""
X = df.drop('price', axis=1)
y = df['price']
print('Shape of X = ', X.shape)
print('Shape of y = ', y.shape)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=51)
print('Shape of X_train = ', X_train.shape)
print('Shape of y_train = ', y_train.shape)
print('Shape of X_test = ', X_test.shape)
print('Shape of y_test = ', y_test.shape)
"""### Feature Scaling"""
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
sc.fit(X_train)
X_train = sc.transform(X_train)
X_test = sc.transform(X_test)
"""## Linear Regression - ML Model Training"""
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
lr.coef_
lr.intercept_
"""## Predict the value of Home and Test"""
X_test[0, :]
lr.predict([X_test[0, :]])
lr.predict(X_test)
y_test
lr.score(X_test, y_test)
"""## Model Evaluation
### Root Mean Squre Error
"""
y_pred = lr.predict(X_test)
y_pred
y_test
from sklearn.metrics import mean_squared_error
import numpy as np
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print('MSE = ', mse)
print('RMSE = ', rmse)
Data not available