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Before data on any platform will become an asset to any organization, it has to pass through processing stage to ensure quality and availability. Afterward, that data has to be available to users (both human and system users). The availability of quality data in any organization is the guarantee of the value that data science (in general) will be to that organization.
We are using the airline on-time performance dataset (flights data csv) to demonstrate these principles and techniques in this hadoop project and we will proceed to answer the below questions -
We will also transform the data access model into time series and demonstrate how clients can access data in our big data infrastructure using a simple tool like the Excel spreadsheet.
In this NLP AI application, we build the core conversational engine for a chatbot. We use the popular NLTK text classification library to achieve this.
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.
Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.
In this spark project, you will use the real-world production logs from NASA Kennedy Space Center WWW server in Florida to perform scalable log analytics with Apache Spark, Python, and Kafka.
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.