Upload app.py

app.py

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+import streamlit as st
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import PolynomialFeatures
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+
+st.title('Polynomial Regression Prediction App')
+
+default_X = "0, 1, 2, -1, -2"
+default_Y = "1, 6, 33, 0, 9"
+
+X_input = st.text_area('Enter the X values (comma-separated):', value=default_X)
+Y_input = st.text_area('Enter the Y values (comma-separated):', value=default_Y)
+
+X = np.array([float(x) for x in X_input.split(',')]).reshape(-1, 1)
+Y = np.array([float(y) for y in Y_input.split(',')])
+
+degree = len(X)-1
+
+poly = PolynomialFeatures(degree=degree)
+X_poly = poly.fit_transform(X)
+
+regressor = LinearRegression()
+regressor.fit(X_poly, Y)
+
+x_values = np.linspace(min(X), max(X), 100).reshape(-1, 1)
+x_values_poly = poly.transform(x_values)
+y_predicted = regressor.predict(x_values_poly)
+
+st.write("### Polynomial Regression Prediction Plot")
+plt.scatter(X, Y, color='red', label='Data')
+plt.plot(x_values, y_predicted, color='blue', label='Predicted')
+plt.title(f'Polynomial Regression Prediction (Degree {degree})')
+plt.xlabel('X')
+plt.ylabel('Y')
+plt.legend()
+st.pyplot()
+
+y_predicted = regressor.predict(X_poly)
+data = {'X': X.ravel(), 'Y': Y, 'Y_pred': y_predicted}
+df = pd.DataFrame(data)
+st.write("### Dataframe with Predicted Values")
+st.write(df)
+
+coefficients = regressor.coef_
+coeff_data = {'Feature': [f'X^{i}' for i in range(1, degree + 1)], 'Coefficient': coefficients[1:]}
+coeff_df = pd.DataFrame(coeff_data)
+st.write("### Coefficients of Polynomial Terms")
+st.write(coeff_df)
+
+
+
+coefficients = [i for i in regressor.coef_]
+terms = [f'{coeff:.3f}X^{i}' for i, coeff in enumerate(coefficients) if coeff != 0]
+
+latex_equation = r'''
+Our Equation: {:.3f} + {}
+'''.format(regressor.intercept_, ' + '.join(terms))
+
+st.write("### Polynomial Equation")
+st.latex(latex_equation)
+
+
+def calculate_polynomial_value(coefficients, X, intercept):
+    result = sum(coeff * (X ** i) for i, coeff in enumerate(coefficients))
+    return result + intercept
+
+X_to_calculate = st.number_input('Enter the X value for prediction:')
+result = calculate_polynomial_value(coefficients, X_to_calculate, regressor.intercept_)
+st.write(f"Predicted Y value at X = {X_to_calculate:.2f} is {result:.2f}")