linear_regression in machine learning import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression

df = pd.read_csv("D:\DATASCIENCE/3-linear-reg/canada_per_capita_income.csv") df

plt.xlabel('years') plt.ylabel('canada_per_capita_income (US$)') plt.scatter(df.year, df.income, color='red', marker='*')

reg = LinearRegression() reg.fit(df[['year']], df.income) # training

reg.predict([[2019]]) #40460.22901919 reg.predict([[2020]]) #41288.69409442 reg.predict([[2021]]) #42117.1591696 reg.predict([[2022]]) #42945.6242448 reg.predict([[2023]]) #43774.08932009 reg.predict([[2024]]) #44602.55439531

#displaying the scatter plot

plt.xlabel('year') plt.ylabel('canada_per_capita_income (US$)') plt.scatter(df.year, df.income, color='red', marker='*') plt.plot(df.year, reg.predict(df[['year']]), color='blue')