Simple Linear Regression is a Supervised machine learning Algorithm coming under the concept of regression.
It is a statistical model that represents the relationship between one independent variable (X) and one dependent variable (y).
In this context the plot should be a straight line which is called as best-fit line or regression line.
It is represented by the regression equation, that is:
Y = m*X + c
where m represents the slope or gradient of the line, this can be positive slope, negative slope or zero, and c represents the y-intercept of the line.
Example: Let we have a csv file named as “homeprice.csv” having two columns like area and price. Here by implementing simple linear regression we will predict the price value by taking an area value. Here we have considered area as X(independent variable) and price as y(dependent variable)
#import libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns
#Create dataframe df=pd.read_csv("E:\dataset\homeprice.csv") print(df)
< class 'pandas.core.frame.DataFrame'> RangeIndex: 5 entries, 0 to 4 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 area 5 non-null int64 1 price 5 non-null int64 dtypes: int64(2) memory usage: 208.0 bytes
#Check missing value df.isnull().sum()
area 0 price 0 dtype: int64
#Scatter plot plt.scatter(df.area,df.price,marker='*',color='red') plt.xlabel("area values") plt.ylabel("price values") plt.show()
#Create LinearRegression model from sklearn.linear_model import LinearRegression obj=LinearRegression()
#Trained the model obj.fit(df[['area']],df.price)
#Predict the value obj.predict([])
#Now we will put m and c value in regression equation y=m*X+c y=135.78767123*4500+180616.43835616432 print(y)
#Plot a bestfit line(regression line) plt.scatter(df.area,df.price,marker='*',color='red') plt.plot(df.area,obj.predict(df[['area']]),color='blue') plt.xlabel("area values") plt.ylabel("price values") plt.show()
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