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Every Departmental store chain like Walmart wants to predict the store sales in the nearby future so that inventory planning can be done. Along with that, sales prediction helps to increase/decrease store staff based on the rush (More sales can mean more customers are coming to the stores). Also, it is always a good idea to do sales and revenue forecasting to better understand the company's cash-flows and overall growth.
For inventory planning, you also need to know what products (or category of products aka department) will be utilised more. Under-stock some products and your sales are hit. Over-stock items like perishables and you run into losses if the product expires. That's why the sales prediction is done at a combination of store and department level (and sometimes even at product level for high-selling products).
In this problem, we have been given the sales data of 45 stores based on store, department and week. The size and type of each store has been provided. Holiday weeks have been marked. Along with these, price markdown data (almost like discount data) has been given. A few macro-indicators like CPI, Unemployment rate, Fuel price etc. are also provided.
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 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 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.
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.
Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.
In this Databricks Azure tutorial project, you will use Spark Sql to analyse the movielens dataset to provide movie recommendations. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.
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.
Learn to design Hadoop Architecture and understand how to store data using data acquisition tools in Hadoop.