I am going to explain to you about getting started with Kaggle and making use of it to master your data science skills. Kaggle is one of the world’s largest community of data scientists and machine learning specialists.
Okoshi is ranked 55 in Kaggle global rankings and currently works as a data scientist at Rist -- an AI company based in Japan.
Hiroki is currently working as a data scientist and is ranked in the top 100 of the world’s largest platforms for data science competition.
Using ARIMA models for Time Series Forecasting .Python Sales Forecasting Kaggle Competition
So…data engineering again! Last week I participated in a Kaggle competition on Mechanisms of Action Prediction. (This competition is still on going, try it if you want!) Basically it asks you to train an algorithm to classify drugs based on their biological activity, and I want to share with you now some quite useful (and simple!) techniques to improve accuracy for tabular data I learned in this competition. Hope it helps! Some useful ways to engineer your data for better performance! Simple Data Engineering to Improve Your Machine Learning Results
Using Convolutional Neural Networks in Tensorflow to Analyse Chest XRays. In this short article, we will show how TensorFlow can be used to easily classify image data using deep neural networks. We will showcase the method using the Chest XRay image dataset available on Kaggle.
Pre-Processing and Applying Machine Learning Algorithms (RandomForest and XGBoost). In this part, I will cover Data Preprocessing and the Application of Supervised Learning Algorithms, namely RandomForest and XGBoost to the prepared training dataset.
Kaggle House Prices Prediction with Linear Regression and Gradient Boosting. This notebook achieved a score of 0.12 and within the top 25% in this Kaggle House Price competition
(Deep) Learning from Kaggle Competitions. I would like to get more practice with deeplearning. Kaggle is great learning environment, partially because it provides a lot of interesting data.
This post is a loving tribute to my daughter, whose eyes are shining stars in these dark and troubled nights. Names evolve. Parents would take ...
Improving Our Code to Obtain Better Results for Kaggle’s Titanic Competition with Data Analysis & Visualization and Gradient Boosting Algorithm. We will explore the dataset using Seaborn and Matplotlib. Besides, new concepts will be introduced and applied for a better performing model.
Martin Henze joined Kaggle to learn more about machine learning, and to use ML tools for his astrophysics projects.
Your Imaginary first Day as a Data Analyst. Finish your first project with the help of your prior online course knowledge
In this article I will outline the process I took in completing the first of the four tasks, Descriptive Analytics.
In this blog-post ,I will go through the process of creating a machine learning model for suv cars dataset. The dataset provides information regarding the age ,gender and Estimated Salary.
Santander Customer Satisfaction — A Self Case Study using Python. Classification modeling on the Santander Customer Satisfaction dataset in Kaggle as part of self case study — Applied AI Course using Python (github link and Linkedin)
Ensembling and Stacking. Stacked Generalization or “Stacking” for short is an ensemble machine learning algorithm.
How To Get High Performing Models In Competitions. Here are some practical tips I’ve accumulated through my Kaggle journey. So, either build your own model or just start from a baseline public kernel, and try implementing these suggestions !
In this article, we’ll go through all the major data augmentation methods for NLP that you can use to increase the size of your textual dataset and improve your model performance.
The magic behind Ensemble Learning. How to boost the performance of your models with a simple technique used by many Kaggle competition winners