Welcome to Just into Data, a place for data science made simpleR!

Enjoy data science articles on various topics such as Machine Learning, AI, Statistical Modeling, Python Programming.

exploratory data analysis flag explore seaborn

How to use Python Seaborn for Exploratory Data Analysis
 Explore an example dataset by Histogram, Heatmap, Scatter plot, Barplot, etc

This is a tutorial of using the seaborn library in Python for Exploratory Data Analysis (EDA).

In this guide, you’ll discover (with examples):

– How to use the seaborn Python package to produce useful and beautiful visualizations, including histograms, bar plots, scatter plots, boxplots, and heatmaps.
– How to explore univariate, multivariate numerical and categorical variables with different plots.
– How to discover the relationships among multiple variables.
– Lots more.

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compass into data science how to learn

How to Learn Data Science Online: ALL You Need to Know
 Python, SQL, Machine Learning, Portfolios plus other Online resources

This is a complete roadmap/curriculum of getting into data science with online resources.

Whether you want to learn for free or more efficiently, this guide will walk you through the step-by-step process that’ll put you on the right path. We’ll talk about skills, online courses, books, and other resources.

You’ll discover:

– the basics of data science (Python, SQL, Machine Learning/Statistics) and How to learn them.
– Why and How to build a data science portfolio.
– other tips/resources to dive into the world of data science.
Start your data science journey today!

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sentiment analysis leaves

How to do Sentiment Analysis with Deep Learning (LSTM Keras)
 Automatically Classify Reviews as Positive or Negative in Python

In this tutorial, we build a deep learning neural network model to classify the sentiment of Yelp reviews.

Following the step-by-step procedures in Python, you’ll see a real life example and learn:

– How to prepare review text data for sentiment analysis, including NLP techniques.
– How to tune the hyperparameters for the machine learning models.
– How to predict sentiment by building an LSTM model in Tensorflow Keras.
– How to evaluate model performance.
– How sample sizes impact the results compared to a pre-trained tool.
And more.

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