Sai Ajay ajaymuktha

## Reshape.py
X_train = X_train.reshape(-1, 28*28)
X_test = X_test.reshape(-1, 28*28)

## Normalze.py

X_train = X_train/255.0
X_test = X_test/255.0

## load.py
(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()

## libraries.py
import tensorflow as tf
from tensorflow.keras.datasets import fashion_mnist

## demo.dart
Scaffold{
appBar: AppBar()
body: Container(
 child: Column(
  children: [
  Row(
      Text(),
      Icon(),
      ), /row
  Text()

## cm.py
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

## knnfit.py
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
classifier.fit(X_train, y_train)

## dataset.py
dataset = pd.read_csv('Sales.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values

## linpoly.py
from sklearn.preprocessing import PolynomialFeatures
poly_reg =  PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, Y)

## polyfit.py
from sklearn.preprocessing import PolynomialFeatures
poly_reg =  PolynomialFeatures(degree = 4)
X_poly = poly_reg.fit_transform(X)
	X_train = X_train.reshape(-1, 28*28)
	X_test = X_test.reshape(-1, 28*28)
	import tensorflow as tf
	from tensorflow.keras.datasets import fashion_mnist
	Scaffold{
	appBar: AppBar()
	body: Container(
	child: Column(
	children: [
	Row(
	Text(),
	Icon(),
	), /row
	Text()
	from sklearn.metrics import confusion_matrix
	cm = confusion_matrix(y_test, y_pred)
	from sklearn.neighbors import KNeighborsClassifier
	classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)
	classifier.fit(X_train, y_train)
	dataset = pd.read_csv('Sales.csv')
	X = dataset.iloc[:, [2, 3]].values
	y = dataset.iloc[:, 4].values
	from sklearn.preprocessing import PolynomialFeatures
	poly_reg = PolynomialFeatures(degree = 4)
	X_poly = poly_reg.fit_transform(X)
	lin_reg_2 = LinearRegression()
	lin_reg_2.fit(X_poly, Y)