R^2 Regression

 

 R^2 Regression 

#101daysofcode

It is a measure of how close the data are to the line of best fit given by are regression model.

1(100%) is best score and 0(0%) is the worst score.

Goodness of fit tell how well do, your model has predicted value

In Simple terms it shows how well the data fit the regression model (the goodness of fit).

How to Calculate R-Squared

The formula for calculating R-squared is:

Where:

• SS_regression is the sum of squares due to regression (explained sum of squares)
• SS_total is the total sum of squares

Practical 

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.impute import SimpleImputer

############## DATA CLEANING #####################################################

df=pd.read_csv(r'path')

# Numerical

num_var=df.select_dtypes(include=['int64','float64']).columns

print(df[num_var])

im=SimpleImputer(strategy='mean')

im.fit(df[num_var])

df[num_var]=im.transform(df[num_var])

print(df[num_var].isnull().sum())

# Categorical

cat_var=df.select_dtypes(include='O').columns

imp=SimpleImputer(strategy='most_frequent')

imp.fit(df[cat_var])

df[cat_var]=imp.transform(df[cat_var])

print(df.isnull().sum().sum())

################# DATA PREPROCESSING ###########################################

df2=df.drop(columns=df[cat_var]) Here you should try using different methods of

data preprocessing and please let me know

if you used

# print(df2)

################## DATA SPlITING ################################################

X=df2.drop(columns='price', axis=1)

y=df2['price']

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=69)

################## 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)

################### TRAINING ####################################################

from sklearn.linear_model import LinearRegression

lr=LinearRegression()

lr.fit(X_train, y_train)

# print(lr.coef_) # used to  print feature coefeciant that our model has learned

print(lr.intercept_)

################# PREDICTION ###############################################

pre=lr.predict(X_test)

print(pre) # the predicted values

print(y_test) # the original values

score=lr.score(X_test, y_test) # shows you the accuracy percentage of your model

print(score*100)

########### R-square #####################################################

from sklearn.metrics import r2_score

y_pre=lr.predict(X_test)

r2=r2_score(y_test, y_pre)

print(r2)