2-Amaliy mashg`ulot

2-Amaliy mashg`ulot.
1. Python muhitida Pandas, Keras, Tensorflow, kutubxonalaridan foydalangan holda jadval ko’rinishida berilgan ma’lumotlarni klassifikatsiya qilish.
2. Logistik regressiya tushunchasi. Xatoliklarni baholash. Klassifikatsiyalashni amalga oshirish.
3. Eng soda perseptron yordamida hisoblashlarni amalga oshirish.

1. 
   Python muhitida Pandas kutubxonasidan foydalangan holda jadval ko’rinishida berilgan ma’lumotlarni klassifikatsiya qilish.
   

1. 
   import requests
   

download_url = "https://raw.githubusercontent.com/fivethirtyeight/data/master/nba-elo/nbaallelo.csv"

target_csv_path = "nba_all_elo.csv"

response = requests.get(download_url)

response.raise_for_status() # Check that the request was successful
with open(target_csv_path, "wb") as f:
f.write(response.content)
print(f.write)


Ma'lumotlarni yuklab olish uchun skript yaratadi hamda ularni tekshirish uchun quidagicha shellga murojaat qilinadi.

>>> import pandas as pd

>>> nba = pd.read_csv("nba_all_elo.csv")
>>> type(nba)

Yuklab olingan sikript ichida qancha malumot uzunligini korish esa

>>> len(nba)

126314
>>> nba.shape
(126314, 23)
Quidagicha tekshiriladi.

2. 
   Kerasda neyron tarmoq qurish (Input)
   

model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
Kerasda neyron tarmoq qurish (Hidden)
model = Sequential()
model.add(Dense(512,
input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
Kerasda neyron tarmoq qurish
(Output) model = Sequential()
model.add(Dense(512, input_shape=(784,)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))

3. 
   Logistik regressiya tushunchasi. Xatoliklarni baholash. Klassifikatsiyalashni amalga oshirish.
   

import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, confusion_matrix

# Step 2: Get data

x = np.arange(10).reshape(-1, 1)
y = np.array([0, 1, 0, 0, 1, 1, 1, 1, 1, 1])

# Step 3: Create a model and train it

model = LogisticRegression(solver='liblinear', C=10.0, random_state=0)
model.fit(x, y)

# Step 4: Evaluate the model

p_pred = model.predict_proba(x)
y_pred = model.predict(x)
score_ = model.score(x, y)
conf_m = confusion_matrix(y, y_pred)
report = classification_report(y, y_pred)

3. Eng soda perseptron yordamida hisoblashlarni amalga oshirish.

import numpy as np

N = 100
w=0

def sigmoid_function(an):
return 1/(1 + np.exp(-an))

# Make a prediction with weights

def predict(row, weights):
activation = weights[0]
for i in range(len(row)-1):
activation += weights[i + 1] * row[i]
return 1.0 if activation >= 0.0 else 0.0

# test predictions

dataset = [[2.7810836,2.550537003,0],
[1.465489372,2.362125076,0],
[3.396561688,4.400293529,0],
[1.38807019,1.850220317,0],
[3.06407232,3.005305973,0],
[7.627531214,2.759262235,1],
[5.332441248,2.088626775,1],
[6.922596716,1.77106367,1],
[8.675418651,-0.242068655,1],
[7.673756466,3.508563011,1]]
weights = [-0.1, 0.20653640140000007, -0.23418117710000003]
for row in dataset:
prediction = predict(row, weights)
print("Expected=%d, Predicted=%d" % (row[-1], prediction))

#short overwiev of updating process of w's

learning_rate = 0.01
s