Regression Analysis in Python

 Regression analysis is a statistical method used to model the relationship between a dependent variable and one or more independent variables. In Python, regression analysis can be performed using various libraries such as scikit-learn, statsmodels, and numpy.

Here are the steps to perform regression analysis in Python using scikit-learn library:

Step 1: Import Libraries Import the required libraries, including numpy, pandas, and scikit-learn.

python
import numpy as np 
import pandas as pd 
from sklearn.linear_model import LinearRegression

Step 2: Load the Data Load the dataset into a pandas dataframe.

python
data = pd.read_csv('data.csv')

Step 3: Prepare the Data Separate the dependent variable and independent variable from the dataset.

python
x = data['independent_variable'].values.reshape(-1,1) 
y = data['dependent_variable'].values.reshape(-1,1)

Step 4: Create the Regression Model Create a LinearRegression model from scikit-learn.

python
regression_model = LinearRegression()

Step 5: Fit the Model Fit the model using the dataset.

python
regression_model.fit(x,y)

Step 6: Make Predictions Use the trained model to make predictions.

python
y_predicted = regression_model.predict(x)

Step 7: Visualize the Results Visualize the results using matplotlib.

python
import matplotlib.pyplot as plt 
plt.scatter(x, y)
plt.plot(x, y_predicted, color='red')
plt.show()

In summary, performing regression analysis in Python using scikit-learn involves importing the required libraries, loading and preparing the data, creating a regression model, fitting the model, making predictions, and visualizing the results.