2

def normalize_data(df):
    min_max_scaler = sklearn.preprocessing.MinMaxScaler()
    df['open'] = min_max_scaler.fit_transform(df.open.values.reshape(-1,1))
    df['high'] = min_max_scaler.fit_transform(df.high.values.reshape(-1,1))
    df['low'] = min_max_scaler.fit_transform(df.low.values.reshape(-1,1))
    df['close'] = min_max_scaler.fit_transform(df['close'].values.reshape(-1,1))
    return df

3

data = np.array(y)
scaler = MinMaxScaler(feature_range=(-1, 1))
train_data_normalized = scaler.fit_transform(data.reshape(-1, 1))

4

## * Note: I scale all features in range of [0,1].

## If you would like to train based on the resampled data (over hour), then used below
values = df_resample.values 


## full data without resampling
#values = df.values

# integer encode direction
# ensure all data is float
#values = values.astype('float32')
# normalize features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
# frame as supervised learning
reframed = series_to_supervised(scaled, 1, 1)

# drop columns we don't want to predict
reframed.drop(reframed.columns[[8,9,10,11,12,13]], axis=1, inplace=True)
print(reframed.head())

5

wltw_stock_prices=wltw_stock_prices.reshape(-1, 1)
scaler = MinMaxScaler(feature_range=(0, 1))
wltw_stock_prices = scaler.fit_transform(wltw_stock_prices)

6

def normalize_data(df):
    min_max_scaler = preprocessing.MinMaxScaler()
    df['open'] = min_max_scaler.fit_transform(df.open.values.reshape(-1,1))
    df['high'] = min_max_scaler.fit_transform(df.high.values.reshape(-1,1))
    df['low'] = min_max_scaler.fit_transform(df.low.values.reshape(-1,1))
    df['volume'] = min_max_scaler.fit_transform(df.volume.values.reshape(-1,1))
    df['adj close'] = min_max_scaler.fit_transform(df['adj close'].values.reshape(-1,1))
    return df
df = normalize_data(df)

7

train = train.sort_values('visit_date')
values = np.log1p(train['visitors'].values).reshape(-1,1)
values = values.astype('float32')

scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)

minmaxscaler

# Scaling the training set
sc = MinMaxScaler(feature_range=(0,1))
training_set_scaled = sc.fit_transform(training_set)