Create a python script that, when given a set of x and y data points, provides a linear regression and curve fit. then displays the curve.
system
ek paythan script banaaen, jise jab eks or vaai data binduon kaa ek set diya jaataa he, to ek raikhik pratigaman or vakr feet pradaan karata he.phir vakr pradarshit karata he.
user
koi baat nahin!yahaan code he jo data binduon ke ek set kaa raikhik pratigaman pradaan karata he or data ke liye ek vakr feet karata he:

enpi ke rup main sankhya ko aayaat karen
PLT ke rup main matplotlib.pyplot aayaat karen

# namoona data utpann karen
x_data = np.linspace (0, 10, 50)
y_data = 2 * x_data + 1 + np.random.normal (0, 1, size = x_data.shape)

# raikhik pratigaman karen
regression_params = np.polyfit (x_data, y_data, 1)

# vakr fitting karen
fit_params = np.polyfit (x_data, y_data, 2)

# feet kiye gaye vakr ke liye ank utpann karen
x_fit = np.linspace (0, 10, 100)
y_fit = np.polyval (fit_params, x_fit)

# mul data, pratigaman line or feet kiye gaye vakr ko plot karen
plt.scatter (x_data, y_data, label = 'data')
plt.plot (x_data, np.polyval (regression_params, x_data), 'r-', label = 'raikhik pratigaman')
plt.plot (x_fit, y_fit, 'g-', label = 'vakr feet')
plt.xlabel ('x')
plt.ylabel ('y')
plt.legend ()
plt.grid (sach)
plt.show ()

yahaan ham udhaaharan data points X or Y data ko yadrichhik sankhyaon ke do saraniyon ko banaakar banaae ja rahe hai, phir fnction "np.polyfit" kaa upyog karke vakr fitting or raikhik pratigaman donon.ant main, code "matploit.pyplot" kaa upyog karke "PLT" ke rup main parinaam pradarshit karata he.
assistant
<|system|>
</s>
<|user|>
ek paythan script banaaen, jise jab eks or vaai data binduon kaa ek set diya jaataa he, to ek raikhik pratigaman or vakr feet pradaan karata he.phir vakr pradarshit karata he.</s>
<|assistant|>
koi baat nahin!yahaan code he jo data binduon ke ek set kaa raikhik pratigaman pradaan karata he or data ke liye ek vakr feet karata he:

enpi ke rup main sankhya ko aayaat karen
PLT ke rup main matplotlib.pyplot aayaat karen

# namoona data utpann karen
x_data = np.linspace (0, 10, 50)
y_data = 2 * x_data + 1 + np.random.normal (0, 1, size = x_data.shape)

# raikhik pratigaman karen
regression_params = np.polyfit (x_data, y_data, 1)

# vakr fitting karen
fit_params = np.polyfit (x_data, y_data, 2)

# feet kiye gaye vakr ke liye ank utpann karen
x_fit = np.linspace (0, 10, 100)
y_fit = np.polyval (fit_params, x_fit)

# mul data, pratigaman line or feet kiye gaye vakr ko plot karen
plt.scatter (x_data, y_data, label = 'data')
plt.plot (x_data, np.polyval (regression_params, x_data), 'r-', label = 'raikhik pratigaman')
plt.plot (x_fit, y_fit, 'g-', label = 'vakr feet')
plt.xlabel ('x')
plt.ylabel ('y')
plt.legend ()
plt.grid (sach)
plt.show ()

yahaan ham udhaaharan data points X or Y data ko yadrichhik sankhyaon ke do saraniyon ko banaakar banaae ja rahe hai, phir fnction "np.polyfit" kaa upyog karke vakr fitting or raikhik pratigaman donon.ant main, code "matploit.pyplot" kaa upyog karke "PLT" ke rup main parinaam pradarshit karata he.</s>
