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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example of simple polynomial regression for single feature.\n",
    "As a dataset I'm using calibration data of high pressure densitometer.\n",
    "My goal was to make an equation, that's can decribe relationship between temperature and period of oscillation of densitometer's U-tube."
   ]

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example of Machine Learning classifications in Python"
   ]
  },
  {

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Kaggle Competition | Titanic: Machine Learning from Disaster\n",
    "The competition's website is located on [Kaggle.com](https://www.kaggle.com/c/titanic)"
   ]
  },

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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preparation with pandas and numpy"
   ]
  },
  {

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PostgreSQL setup instruction

Only for Ubuntu, because I use Ubuntu on

1. Install postgreSQL:

sudo apt install postgresql postgresql-contrib

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import numpy as np
import pandas as pd
import requests

""
LOAD DATA
"""
def get_market_data(ticker):
    """
    Ticker in format quote/base,

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import numpy as np
import pandas as pd
import requests

def get_market_data(ticker):
    """
    Ticker in format quote/base,
    for example 'BTC/USD'
    
    return: pandas dataframe

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import numpy as np
import pandas as pd
import requests
from sklearn.linear_model import BayesianRidge

#load market data
market_data = get_market_data('BTC/USD')

#shift target variable one day back 
#in order to train model to predict one day into future

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import numpy as np
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential
from keras.optimizers import Adam
from logger import logger
import matplotlib.pyplot as plt

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