Py.Cafe
🤖 ML Playground: Regresie Polinomială
- app.py
- requirements.txt
app.py
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import sklearn
import streamlit as st
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
st.title("🤖 ML Playground: Regresie Polinomială")
# 1. Generare date cu zgomot (Noise)
st.sidebar.header("Parametri Date")
noise = st.sidebar.slider("Nivel de zgomot", 0.0, 1.0, 0.2)
n_points = 50
np.random.seed(42)
X = np.sort(5 * np.random.rand(n_points, 1), axis=0)
y = np.sin(X).ravel() + noise * np.random.randn(n_points)
# 2. Configurare Model ML
st.sidebar.header("Setări Model")
grad_polinomial = st.sidebar.selectbox("Gradul Polinomului", [1, 2, 3, 5, 10])
# Antrenare model
poly = PolynomialFeatures(degree=grad_polinomial)
X_poly = poly.fit_transform(X)
model = LinearRegression()
model.fit(X_poly, y)
# Predicție pentru linia graficului
X_plot = np.linspace(0, 5, 100)[:, np.newaxis]
y_plot = model.predict(poly.transform(X_plot))
# 3. Vizualizare
fig, ax = plt.subplots()
ax.scatter(X, y, color='darkorange', label='Date reale (cu zgomot)')
ax.plot(X_plot, y_plot, color='blue', linewidth=2, label='Predicție Model')
ax.set_title(f"Model antrenat (Grad {grad_polinomial})")
ax.legend()
st.pyplot(fig)
# Afișare metrici simple
mse = np.mean((model.predict(X_poly) - y) ** 2)
st.metric("Eroarea Medie Pătratică (MSE)", f"{mse:.4f}")