# Intro

When we want to measure Time-To-Event Data Analysis (or time between events for that matter), time itself is not necessarily an observation axis rather than the observed target. How can I model how much time between events can…

# 1. Theory

To compute these models, we need to vectorize our variables and align them in a way that every column is a variable (discretized) and every row is an observation of the variables in a given point of a common axis. This is mandatory for applications (such as machine learning) which require a tidy dataset as input.

To show it in a simple example:

Above, two variables are both graphed and vectorized sharing a common axis…

# Intro

Then we’ll significantly improve that model by generating features from external data such as proximity of cultural spaces, parks, public art spots, golf courses, swimming beaches, picnic tables, etc.. measuring the improvement from each added feature.

# What we’ll do

• Step 1: Explore the Seattle Housing Prices Data
• Step 2: Create a Price Prediction Model
• Step 3: Add Features from External Data
• Step 4: Compare and Analyze Results

# How to Boost Your Model’s Accuracy When All Else Has Failed

A simple approach.

You will either step forward into growth or you will step backward into safety.

-Abraham Maslow-

# Outstanding Returns Using Sentiment Analysis to Predict Alphabet (Google) Stock Price

## Simplicity is key

Simplicity is the keynote of all true elegance.

-Abraham Maslow-

# The Next Industry Shift for Machine Learning Performance Enhancement

## It’s simpler than you think.

People who are crazy enough to think they can change the world are the ones who do.

-Rob Siltanen-

# Intro

Thousands of companies around the world, from small startups to global corporations, find great value in improving the performance of their supervised or unsupervised ML models, whether it’s a sales or demand forecast, a market basket analysis…

# Create a Demand Sales Forecast Model and Minimize the Error

A step-by-step tutorial in Python.

Sales or Demand Forecasts are a priority on a huge amount of companies (from startups to global corporations) Data Science/Analytics departments. To say the least, there is a low supply of experts in the subject. Reducing the error even by a small amount can make a huge difference in revenue or savings.

In this article, we’ll do a simple sales forecast model with real data and then improve it by finding relevant features using Python.

# What we’ll do

• Step 1: Define and understand Data and Target
• Step 2: Make a Simple Forecast Model
• Step 3: Improve it by…

# Outstanding Performance predicting Amazon Stock applying Natural Language Processing (Python)

Simplicity is key.

# Goal

We’ll also measure how profitable it would be in real life.

# What we’ll do

• Step 1: Set up technical prerequisites
• Step 2: Get the data for daily Amazon Candles since 2017
• Step 3: Define and understand target for ML
• Step 4: Blend business news to our data and understand tokens
• Step 5: Prepare our data and apply ML
• Step 6: Measure and analyze results
• Step 7: Break the data and train/test through time

• Have Python…

# Predict Any Cryptocurrency Applying NLP using Global News

## A step-by-step tutorial using Python.

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On today’s harsh global economic conditions, traditional indicators and techniques can have poor performances (to say the least).

In this tutorial we’ll search for useful information on news and transform it to a numerical format using NLP to train a Machine Learning model which will predict the rise or fall of any given Cryptocurrency…

# What Drivers & Indicators Will Decide the 2020 US Elections?

## Intro

The campaigns for the 2020 US Presidential Election have started and roughly 4 months from now (November 3rd is Election Day) a head of state will be selected by the voters.

Which candidate will leverage the concepts that influence the voters’ behavior the most?

Due to the US Electoral College system, it’s highly likely that the election will be defined by the swing states. Currently, the polls are reasonably unanimous on the following:

There are 21 states that lean Democrat

There are 24 states that Lean Republican

And there are 6 swing states where voting preference is still up in…

## Federico Riveroll

An attempt to separate signal from noise. | MSDS

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