Ma’ruza rejasi

Ko’p xususiyatli (o’zgaruvchili)

Chiziqli regressiya

Yuza(m2)	Nariz($1000)
2104	460
1416	232
1534	315
852	178
…	…

Bir xususiyatli (o’zgaruvchili) bo’lganda

Yuzasi(m2)	Xonalar soni	Qavatlar soni	Yoshi (yil)	Narxi ($1000)
2104	5	1	45	460
1416	3	2	40	232
1534	3	2	30	315
852	2	1	36	178
…	…	…	…	…

Ko’p xususiyatli (o’zgaruvchili).

Ko’p xususiyatli(o’zgaruvchili).

Belgilanadi:

= xususiyatlar nomeri

= o’zratuvchi tanlamaning i- xususiyatlari

= i- o’zratuvchi tanlamaning xususiyatlarining j- xususiyati

Yuzasi(m2)	Xonalar soni	Qavatlar soni	Yoshi (yil)	Narxi ($1000)
2104	5	1	45	460
1416	3	2	40	232
1534	3	2	30	315
852	2	1	36	178
…	…	…	…	…

Gepoteza:

Oldin:

Bu yerda deb qaralishi kerak.

Gepoteza:

Cost funksiyasi:

Parametrlar:

Takrorlash

Gradient descent:

( yaqinlashguncha)

Gradient Descent

Takrorlash

Oldingi holda(n=1):

Yangi algoritmda :

Takrorlash

( yaqinlashguncha )

Misol. = yuza(0-2000 m2)

= Xonalar soni(1-5)

Xususiyatlar shkalasi

G’oya: Turli shkaladagi xususiyatlarni baravarlashtirish.

yuza(m2)

Xonalar soni

Foydalanilgan adabiyotlar

Aurelian Geron, Hands on Machine Learning with Scikit-Learn Keras&Tensorflow // Second edition Concepts, Tools, and Techniques to Build Intelligent Systems, 2019, 510 pages

https://www.geeksforgeeks.org/ml-types-learning-supervised-learning/ https://www.guru99.com/unsupervised-machine-learning.html https://www.w3schools.com/python/python_ml_linear_regression.asp https://www.w3schools.com/python/python_ml_multiple_regression.asp https://www.w3schools.com/python/python_ml_polynomial_regression.asp https://www.mathworks.com/help/stats/regress.html