Intro

In this article, we’ll create a forecasting model to predict housing prices in Seattle. We will first make a model using the properties’ attributes such as sqft, rooms, bedrooms, bathrooms, view, etc.

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

Step 1. Explore the Seattle Housing Prices Data

To make the model, we’ll use a ‘Seattle Housing Prices’ taken from this House Prediction Project. It contains 21,613 registers of prices from 2015, with several features from the houses. …

A simple approach.

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

-Abraham Maslow-

Context

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 recommender, a customer classifier, a sales optimizer, a chatbot, an algorithmic trading pipeline, a document labeler, an elections forecast, a spam filter, a medical diagnosis solution, a route optimizer, a face recognizer or a self-driving car. …

“Simplicity is the keynote of all true elegance.”

-Abraham Maslow-

*Disclaimer: I am assuming that whoever has the ability to comprehend and execute the content of this article is savvy enough to perform a robust back-testing in every corner of their trading pipeline before actually running it in production. However, there are some considerations that this article doesn’t take into account (spread, slippage and transaction costs, among others) and for that matter, this article is not to be considered financial advice. It is to be considered an educational step towards better performing results.

Intro

The goal of this article is to predict if the stock price for Alphabet (GOOGL) will be higher or lower at the end of any given day, using news from the closing time of the previous day’s trading session (4 pm EST for the NYSE) to before market hours of that day (9:30 am EST). …

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

-Rob Siltanen-

Intro

For the last decade, advances on improvement of models have been increasing on many directions because the demand for visible performance is reachable on a global scope. Decision makers don’t need to be Statisticians to understand the value of increasing revenue or decreasing costs.

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 recommender, a customer classifier, a sales optimizer, a chatbot, an algorithmic trading pipeline, a document labeler, an elections forecast, a spam filter, a medical diagnosis solution, a route optimizer, a face recognizer or a self-driving car. …

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 adding New and Financial…

Simplicity is key.

Goal

In this tutorial we’ll make a Machine Learning Pipeline that inputs News and applies NLP to generate predictions for Amazon Stock Price re-training through time.

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

Step 1. Prerequisites

• Have Python 3.1 (or higher)…

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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 (using Python). …

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 the…