Predictive Model with Linear Regression

 This demo program will show how to create a predictive model using Linear Regression.

The Ouput:

Predicted value when x = 100:

The code:

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from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np import joblib import warnings warnings.filterwarnings('ignore') X = np.array([5, 10, 20, 30, 40, 50, 10, 25, 30, 45, 50, 15, 20, 35, 40, 50, 50, 45, 30, 25, 20, 10, 66]) Y = np.array([50, 95, 185, 280, 370, 490, 100, 230, 290, 410, 500, 135, 200, 295, 395, 495, 480, 430, 305, 205, 175, 110, 600]) data = pd.DataFrame({'X':X, 'Y':Y}) data.head() sns.jointplot(X, Y, kind='reg') plt.show() sns.regplot(x='X', y='Y', data=data) plt.title("Regression Plot XY") model = LinearRegression().fit(X.reshape(-1, 1), Y) filename = "model.sav" joblib.dump(model, filename) loaded_model = joblib.load(filename) loaded_model.predict([[100]])