Edna  Bernhard

Edna Bernhard

1596855420

Melanoma Tumor Size Prediction: Weekend Hackathon

MachineHack welcomes all Data Science and Machine Learning enthusiasts to another exciting weekend hackathon. This weekend, participants must use their data science skills to predict the melanoma tumor size based on a number of factors.

#featured #datascience hackathons #hackathon #machine learning hackathon #machine learning hackathons

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Buddha Community

Melanoma Tumor Size Prediction: Weekend Hackathon
Edna  Bernhard

Edna Bernhard

1596855420

Melanoma Tumor Size Prediction: Weekend Hackathon

MachineHack welcomes all Data Science and Machine Learning enthusiasts to another exciting weekend hackathon. This weekend, participants must use their data science skills to predict the melanoma tumor size based on a number of factors.

#featured #datascience hackathons #hackathon #machine learning hackathon #machine learning hackathons

Trevor  Russel

Trevor Russel

1618115820

New Hackathon For Data Scientists — Workation Price Prediction Challenge

MachineHack, in association with Analytics India Magazine, has come up with yet another hackathon for the machine learning community — the Workation Price Prediction Challenge.

In the light of the new normal, different websites have started providing packages to work from different locations. The concept of workation — a portmanteau of work and vacation– is gaining currency. However, it is challenging to find a good place with all the amenities, including high-speed internet and a comfortable stay within the budget.

Thus, to solve the real-world problem of finding the best deals for workations, MachineHack is challenging the machine learning community to build a model for predicting the price per person for workation trips.

To facilitate this, MachineHack has collected workation packages in and around India — starting from Kashmir to Kanyakumari and from Gujarat to Assam. The data has more than 18000+ rows of different packages with details like start location, hotel type, cost per person, destination, itinerary, and many more. Using this dataset, along with the knowledge of machine learning, deep learning, and model building, the participants need to create a model that can efficiently and accurately predict a workation trip’s expense.

#data science hackathon #hackathon #hackathon for data scientists #machine learning hackathon #ml hackathon #predicting workation price #workation price prediction challenge

Melanoma Tumor Size Prediction: Weekend Hackathon #15

MachineHack welcomes all Data Science and Machine Learning enthusiasts to another exciting weekend hackathon. This weekend, machinehackers must use their data science skills to predict the melanoma tumor size based on a number of factors.

Read morE: https://analyticsindiamag.com/melanoma-tumor-size-prediction-weekend-hackathon-15/

#hackathon #machinelearning

Ian  Robinson

Ian Robinson

1623223443

Predictive Modeling in Data Science

Predictive modeling is an integral tool used in the data science world — learn the five primary predictive models and how to use them properly.

Predictive modeling in data science is used to answer the question “What is going to happen in the future, based on known past behaviors?” Modeling is an essential part of data science, and it is mainly divided into predictive and preventive modeling. Predictive modeling, also known as predictive analytics, is the process of using data and statistical algorithms to predict outcomes with data models. Anything from sports outcomes, television ratings to technological advances, and corporate economies can be predicted using these models.

Top 5 Predictive Models

  1. Classification Model: It is the simplest of all predictive analytics models. It puts data in categories based on its historical data. Classification models are best to answer “yes or no” types of questions.
  2. Clustering Model: This model groups data points into separate groups, based on similar behavior.
  3. **Forecast Model: **One of the most widely used predictive analytics models. It deals with metric value prediction, and this model can be applied wherever historical numerical data is available.
  4. Outliers Model: This model, as the name suggests, is oriented around exceptional data entries within a dataset. It can identify exceptional figures either by themselves or in concurrence with other numbers and categories.
  5. Time Series Model: This predictive model consists of a series of data points captured, using time as the input limit. It uses the data from previous years to develop a numerical metric and predicts the next three to six weeks of data using that metric.

#big data #data science #predictive analytics #predictive analysis #predictive modeling #predictive models

Tyshawn  Braun

Tyshawn Braun

1599586380

Product Sentiment Classification: Weekend Hackathon

We are back with another weekend hackathon and this weekend we are challenging the machinehack community to build an NLP model to analyze sentiments in the product reviews for various electronic products.

Analyzing sentiments related to various products such as Tablet, Mobile and various other gizmos can be fun and difficult especially when collected across various demographics around the world. In this weekend hackathon, we challenge the machinehackers community to develop a machine learning model to accurately classify various products into 4 different classes of sentiments based on the raw text review provided by the user. Analyzing these sentiments will not only help us serve the customers better but can also reveal a lot of customer traits present/hidden in the reviews.

The challenge will start on 4th Sep Friday at 6 pm IST.

Click here to participate

Problem Statement & Description

The sentiment analysis requires a lot to be taken into account mainly due to the preprocessing involved to represent raw text and make them machine-understandable. Usually, we stem and lemmatize the raw information and then represent it using TF-IDF, Word Embeddings, etc. However, provided the state-of-the-art NLP models such as Transformer based BERT models one can skip the manual feature engineering like TF-IDF and Count Vectorizers.

The dataset collected has close to 9000 rows with 4 columns and the reviews are in the form of raw text. The labels for each review are provided with the training labels such as positive, negative, no sentiment, and can’t be said(neutral sentence).

In this short span of time, we would encourage you to leverage the ImageNet moment (Transfer Learning) in NLP using various pre-trained models to classify the product reviews correctly using Multi-class Log Loss as a metric.

Given are raw customer reviews over various types of products with 4 different sentiment classes. Your objective as a data scientist is to build a natural language processing model that can accurately classify the class of sentiments as close as possible.


#hackathons #machine learning hackathon #machinehack hackathon #machine-learing