## The growing role of Automated Valuation Models (AVM) in real estate

As real estate moves towards digitization and automation, Automated Valuation Model (AVM) replaces traditional valuation methods. . What is AVM? How is it used in real estate valuation?

## Deep Learning for weather classification

The main aim of this project is to make a model that correctly classifies the weather states on the images it sees.
This seems pretty easy but the main challenge the model will face is that it doesn’t need to learn about the shapes of the objects in the image.

## How to build KNN from scratch in Python

k-Nearest Neighbors (KNN) is a supervised machine learning algorithm that can be used for either regression or classification tasks. KNN is non-parametric, which means that the algorithm does not make assumptions about the underlying distributions of the data.

## Hands-on guide to plotting a Decision Surface for ML in Python

Matplotlib provides a handy function called contour(), which can insert the colors between points. However, as the documentation suggested, we need to define the grid of points Xof yin the feature space. The beginning point would be to find the maximum value and minimum value of each feature then increase by one to make sure that the whole space is covered.

## Clickbait – Prevention by Boosting ensemble Machine Learning algorithms for classification

Clickbait is false advertising links whose purpose is to get clicked at any cost. See how the problem has been tackled through Natural Language Processing and text classification to reduce the level of fake links.

## Ensemble models for Classification

Meta Learners: An algorithm that is used to combine the base estimators is called the meta learner. We can determine how we want this algorithm to respond to different predictions from other models (classifiers in this case).

## Twitter US Airline Sentiment Analysis

Feedback analysis with Lightgbm classifier. Objective: to analyze travelers ‘feelings’ on Twitter posts. It would be fascinating for airlines to use this free data to provide better service to their customers.

## Classification of hate speech and offensive language using machine learning

Use of hate speech and offensive language on social media is a growing challenge. This can effect the mental health of a person against whom the hate speech or offensive language is being expressed, with sometimes devastating effects. In order solve or help solving this problem, text classification can be used, so a message on social media can be tagged as hate speech or offensive language.

## Predicting Sentiment of employee reviews

In this article, I would like to take our dataset a step further to solve a sentiment classification problem. Specifically, we will be assigning sentiment targets to each review and then using a binary classification algorithm to predict those targets.

## Insights on classifier combination

As the arsenal of classification algorithms increased dramatically, it became more and more tempting to use several classifiers and then combine their decisions to gain accuracy and avoid the burden of choosing the right one.

## Hyperparameter Optimization with Scikit-Learn, Scikit-Opt and Keras

Explore practical ways to optimize your model’s hyperparameters with grid search, randomized search, and bayesian optimization.