LET'S DO SOME DATA WORK.
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.
How to apply Unsupervised Anomaly Detection on bank transactions
A use case of Unsupervised Learning with Python, step-by-step
In this tutorial, we’ll show how to detect outliers or anomalies on unlabeled bank transactions with Python.
– How to identify rare events in an unlabeled dataset using machine learning algorithms: isolation forest (clustering).
– How to visualize the anomaly detection results.
– How to fight crime with anti-money laundering (AML) or fraud analytics in banks
Use case and tip from people with industry experience
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.
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.
– 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!
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.
3 Steps to Time Series Forecasting: LSTM with TensorFlow Keras
A Practical Example in Python with useful Tips
We present a deep learning time series analysis example with Python. You’ll see:
– How to preprocess/transform the dataset for time series forecasting.
– How to handle large time series datasets when we have limited computer memory.
– How to fit Long Short-Term Memory (LSTM) with TensorFlow Keras neural networks model.
Hyperparameter Tuning with Python: Keras Step-by-Step Guide
Why and How to use with an example of Keras
This is a practical guide to Hyperparameter Tuning in Python.
To improve your model’s performance, learn:
– why you should use this machine learning technique.
– how to use it with Keras and Tensorflow step-by-step with Python.