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If you are a Machine Learning, Artificial Intelligence, or computer science enthusiast, you probably understand the concept and need for data to help improve a given system or service.
Tech giants and multinational companies make use of large volumes of data to enhance customer experience and their overall quality of service by adopting advanced techniques of business intelligence to make sense of their data. One of the emerging and most important techniques is referred to as predictive analytics.
This article goes over the idea of predictive analytics tools, their application, and a number of examples of open-source tools that you can use!
What are Predictive Analytics Tools?
Predictive analytics tools are software that determine patterns and trends by analyzing and extracting information from an existing dataset. These tools make use of a variety of statistical techniques including data mining, predictive modeling, and Machine Learning to analyze the given data and make predictions.
These tools can be utilized to make sense of patterns in consumer behavior and previous trends to create a plan for a specific time duration to increase the profitability and success of a given service.
Applications of Predictive Analytics
There are many applications of predictive analytics tools that range over a number of fields, including:
- Analyzing customer data to group people based on their buying preferences and then predicting the likeliness of these groups to purchase products.
- Predicting the Return Of Investment (ROI) of targeted marketing campaigns.
- Collecting data from trendy online stores like Amazon Marketplace.
Social Media Marketing
- Planning the type and kind of content to post.
- Predicting the best day and time to post the given content.
- Handling Google Ads and advertisements in general.
Banking and Insurance
- Figuring out credit ratings.
- Identifying fraudulent activities.
- Monitoring health in general.
- Identifying early signs of health problems in an individual.
- Managing inventory and supply chains.
- Aiding in the shipping and fulfillment process.
Open-Source Predictive Analytics Tools
Orange is a data visualization and analytics tool that performs predictive analytics through visual programming or Python scripting. This toolkit is imported as a Python library and includes components for Machine Learning, bioinformatics, text mining, and other data analytical characteristics.
- Interactive data visualization and graphical representation features.
- Includes visual programming.
- Canvas-based Graphical User Interface (GUI) makes it easy to use for beginners.
- Able to execute simple and complex data analytics.
An open-source data science Python and R distribution platform with over 250 different popular packages used to simply package management and deployment. This distribution makes use of data science, Machine Learning applications, and large-scale data processing to perform predictive analytics.
- Advanced analytics, use of workflows, and data interaction.
- Connect all data sources to extract the most value from data.
- Create predictive analytic models with Python, R, and Jupyter Notebooks.
- Integrate your predictive analytic models into intelligent web apps and interactive visualizations.
- Collaborate across entire data science teams using Anaconda.
The R environment is used for statistical computing and graphics. It compiles and runs on a variety of operating systems including UNIX, Windows, and MAC OS. This environment has a large collection of intermediate tools for data analytics and the graphical display of data analytics.
- Includes a variety of statistical models and graphical techniques for predictive analytics.
- Effective data handling and storage facilities.
- A suite of operators for complex data array calculations and statistical analytics.
- Support available online from the R community.
This is a Machine Learning library for the Python programming language. It includes various classification, regression, and clustering algorithms including Support Vector Machines (SVMs), random forests, and k-means clustering which are very useful for predictive modeling. However, advanced programming knowledge is required to be able to perform predictive analytics using Scikit-Learn.
- Advanced data handling includes displaying data in visual and tabular form, arranging data into feature matrices or target vectors.
- A number of classification, regression, and clustering models available for predictive analytics.
- Multiple accuracy metrics to test predictive model performance.
- Data preprocessing and visualization techniques.
- Data classification, regression, and clustering algorithms.
- Extensive association rules to predict trends in data.
- Portable and memory space-friendly software.
A simple and extensible programming environment and framework for building scalable and performant Machine Learning algorithms. The environment includes a number of pre-made Scala, Apache Spark, and Apache Flint algorithms. This environment uses Samsara, a vector math experimentation similar to the R language which works at scale.
- Collaborative filtering to build recommendation systems.
- Clustering and classification algorithms for predictive modeling.
- Supports frequent itemset timing for advanced data extraction.
- Linear algebra operator and distributed algebra optimizer for advanced statistical analytics.
- Builds scalable algorithms for predictive analytics.
7. GNU Octave
This software represents a high-level language intended for numerical computations. This software has a powerful math-oriented syntax with built-in plotting and visualization tools for advanced data analytics. GNU Octave is compatible with MATLAB scripts and operating systems including GNU/Linux, MAC OS, and Windows.
- Built-in 2D/3D data plotting and visualization tools.
- Supports a number of GNU statistical packages for data analytics.
- Makes use of math-oriented predictive modeling.
- Ability to run MATLAB predictive models and Machine Learning algorithms.
A collection of open-source Python-based software used for technical and scientific computing. SciPy features core packages that provide computing tools for Python. It makes use of advanced data handling techniques and predictive models including k nearest neighbor, random forest, and neural networks.
SciPy is available as a Python library in many Python distributions and is a package in Anaconda.
- Modules for optimization, linear algebra, integration, interpolation, special functions, FFT, and ODE solvers.
- Offers various functions for signal, image, and data processing.
- Supports NumPy and Matplot.
You should now have a good idea about open source predictive analytics tools, their applications, and how they make use of advanced techniques to make predictions through data.
All the mentioned tools are completely free to use and available to everyone. If you’ve used these tools before, let us know about your experience in the comments.