Simple Linear Regression in 2 min😎😎

Ok, Let’s see how to implement Simple Linear regression in Python within 2 min.

Step 1 :

Import Libraries

import numpy as nm

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.metrics import r2_score

from sklearn.model_selection import train_test_split

Step 2 :

Import the Dataset

data_set = pd.read_csv(“Path to your data”)

X = data_set.iloc[:,:-1].values

y = data_set.iloc[:,-1].values

Step 3 :

Splitting the datasets as train and test datasets and training the data

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)

l_reg = LinearRegression(X_train,y_train)

l_reg(X_train,y_train)

Step 4:

Visualize the predicted data

plt.scatter(X_train,y_train,color=’red’)

plt.plot(X_train,reg.predict(y_train),color=’blue’)

plt.show()

Step 5:

Calculating the R-Square

y_pred = l_regt(y_train)

r2_score(y_train,y_pred)

If your R2 value is greater (near 1 or above 0.85) you are lucky or else try tuning 😀