victor-fdez

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Seed for reproducibility
np.random.seed(42)

# Generating random sales data for a year for three taco types
days = 365

victor-fdez

import numpy as np
from keras.models import Sequential
from keras.layers import SimpleRNN, Dense
from sklearn.preprocessing import MinMaxScaler

# Generating data for 30 days
np.random.seed(0)
days_of_week = np.tile(np.arange(0, 7), 5)[:30]
weather = np.random.rand(30)
special_promotions = np.random.randint(0, 2, 30)

victor-fdez

from sklearn.linear_model import Perceptron
import numpy as np

# Flavor profile of the taco [spice, sweetness, tanginess]
training_inputs = np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0], [1, 1, 1]])

# Corresponding labels for the flavors [not spicy, spicy, not spicy, spicy]
labels = np.array([0, 1, 0, 1])

# Initialize and train the Perceptron

victor-fdez

from sklearn.linear_model import Perceptron
import numpy as np

# Let's represent 'beef' as 1 and 'chicken' as 0
# and 'spicy' as 1 and 'mild' as 0

# Input data: [spice_level, type_of_meat]
X = np.array([
    [1, 1],  # Spicy beef taco
    [0, 1],  # Mild beef taco

victor-fdez

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np

# let's assume these are our taco features and the target column is whether the person liked the taco or not
taco_data = pd.DataFrame({
    'spice_level': np.random.randint(1, 10, 100), # on a scale of 1-10
    'meat_type': np.random.choice(['beef', 'chicken', 'pork'], 100), # type of meat
    'serving_size': np.random.choice(['small', 'medium', 'large'], 100), # serving size

victor-fdez

# Import necessary libraries
from sklearn.linear_model import LinearRegression
import numpy as np

# Assume these are the temperatures (in degree Fahrenheit)
temperatures = np.array([60, 65, 70, 75, 80, 85, 90]).reshape((-1, 1))

# And these are the corresponding taco prices (in dollars)
taco_prices = np.array([1.5, 1.6, 1.7, 1.9, 2.0, 2.1, 2.3])

victor-fdez

{
  "id" : 229781,
  "resource_state" : 3,
  "*name" : "Hawk Hill",
  "*activity_type" : "Ride",
  "*distance" : 2684.82,
  "*average_grade" : 5.7,
  "*maximum_grade" : 14.2,
  "*elevation_high" : 245.3,
  "*elevation_low" : 92.4,

victor-fdez

Keybase proof

I hereby claim:

• I am victor-fdez on github.
• I am vjft (https://keybase.io/vjft) on keybase.
• I have a public key ASA5qEBpjt2MsREv55ImjdbWyXtXJBCsttyMHFfi3azAyAo

To claim this, I am signing this object:

victor-fdez

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