In this personal Superstore Sales SQL Data Analysis project, an exploratory data analysis was performed on the Superstore Sales Data available on Kaggle. The main aim of the project is to uncover insights into the store's sales and profits trends and patterns from 2014 to 2017.

A complete exploratory data analysis (EDA) and forecasting project focused on retail sales data. The project identifies key sales patterns, seasonal trends, and builds predictive models to forecast future demand at the item-store level.

Exploratory Data Analysis of Sales dataset from an electronics store chain in the US and answering a set of real-world business questions using Python and Data Analytics

INTRODUCTION: The main aim of the project is the management of the database of the pharmaceutical shop. This project is insight into the design and implementation of a Pharmacy Management System. This is done by creating a database of the available medicines in the shop. The primary aim of pharmacy management system is to improve accuracy and enhance safety and efficiency in the pharmaceutical store. The aim of this project is to develop software for the effective management of a pharmaceutical store. We have developed this software for ensuring effective policing by providing statistics of the drugs in stock. Description on the topic: This program can be used in any pharmaceutical shops having a database to maintain. The software used can generate reports, as per the user’s requirements. The software can print invoices, bills, receipts etc. It can also maintain the record of supplies sent in by the supplier. Here, the admin who are handling the organization will be responsible to manage the record of the employee. Each employee will be given with a separate username and password. Problem Definition: The aim of the project is to create an effective software to help the pharmacist to maintain the records of the medicines, handle user details, generate invoice, check and renew validity and provide a scope of communication between users by using inbuilt messaging system. Pharmacy management system deals with the maintenance of drugs and consumables in the pharmacy unit. This pharmacy management system is user friendly. Objectives -> Primary objective •To gain practical experience by modeling a software based on real world problem. •To understand how to work on Front-end (Java) and Back-end (MySQL) by using server(wamp). -> Secondary objective •To develop an application that deals with the day to day requirement of any pharmacy. •To develop the easy management of the medicines (drugs). •To handle the inventory details like sales details, purchase details and stock expiry and quantity. •To provide competitive advantage to the pharmacy. •To provide details information about the stock on details necessary and help locate it in shop easily. •To make the stock manageable and simplify the use of inventory in the pharmacy. Hardware and software tools: The system services and goals are established by consultation with system user. They are then defined in details and serve as a system specification. System requirement are those on which the system runs. ⚙️ Hardware Requirements: o Computer with either Intel Pentium processor or AMD processor. o 1GB+ DDR RAM o 40GB hard disk drive 💻 Software Requirements: o Windows/ MacOS/ Linux operating system. o JRE and JDK. o MySQL server (WAMP or XAMPP or any) Chapter 2 - DESIGN Database Design is a collection of processes that facilitate the designing, development, implementation and maintenance of enterprise data management systems. It helps produce database systems: o That meet the requirements of the users o Have high performance. Architecture Description The design of a DBMS depends on its architecture. It can be centralized or decentralized or hierarchical. The architecture of a DBMS can be seen as either single tier or multi-tier. ER Diagram image.png Fig 1: ER Diagram An entity–relationship model describes interrelated things of interest in a specific domain of knowledge (Refer Fig 1). It is composed of entity types and specifies relationships that can exist between instances of those entity types. Relational Schema Diagram image_1.png Fig 2: Relational Schema Relational schema is a collection of meta-data. Database schema describes the structure and constraints of data representing in a particular domain (Refer Fig 2). Chapter 3 - IMPLEMENTATION Description on Implementation The goal of this application is to manage the medicines and various function of the pharmacy. List of modules: o Login page o Home page o Company o Purchase o Drugs o Sales o User/Settings o Messaging Chapter 4 - Result and Discussion By using MySQL commands and its database this website Pharmacy management tends to store all the data received from the users including drugs sales details and the profit made by the owners are all in this data base. This website allows the user to generate invoices for sales, check expiry and quantity remaining of the drugs. It also provides user with options to renew validity and add more drugs into the store and update the database accordingly. By using xampp server these database commands are easily initiated into the database and the ER diagram with relational schema diagrams helps us to make the structure of the database faster and it was easier to make them understand the needs of the website. Login Information id :1 password: admin CONCLUSIONS AND FUTURE SCOPE o Detailed information gathering has to be done. Without that the purpose for using the software won’t be satisfied properly. o However, it can give good profits in the long run. o Implementing the software requires change in the business practices. o Efficient organization of all knowledge is the analysis company and easy analysis access and retrieval of information is possible. o In this project we can also include BAR CODE facility using the bar code reader, which will detect the expiry date and the other information about the related medicines. o Company using this software will always be able to plan in future and always be aware of their financial position in the market. o It leads to ease in functioning of business processes. o The project can be made more robust by including biometric verification. o There is also a scope to expand by implementing newer technologies like cloud etcetera.

“My name is Gregory Guy. I have just purchased a video store, and I need an up to date, GUI driven system to keep track of all the stock in my store. I am not happy with the existing system where everything is done by hand. “Currently, the store operates on a cash basis, although a contract system might be in the pipeline. You will be contacted to do this at a later stage, if necessary. I have a shop next door that sells sweets, drinks, chocolates etc, which runs from a separate cash register. This should not be included in the system you develop. “My store not only stocks videos, but also video machines, as well as DVD’s. At a later stage, I would like to also stock Sony PlayStation games, controls, and possibly other stock items. I want to be able to add these into the stock list with the minimum of hassle, and without calling in the help of a programmer / system designer. “I want to store all transactional information in a database, so that my accounting system can interface with the data. “I charge as follows: New Release: (Video or DVD) R16 Older Stock: (Video or DVD) R12 • Video Machine R30 • Video Machine & any two videos: R50 “When I start stocking PlayStation games and/or consoles (or any other stock items), I would probably want to have a two-tier pricing system for them as well (where I can charge more for newer stock). “It would also be nice to be able to change my prices if and when I need to. I therefore would like the ability to change the price of a ‘New Release’, and that should affect all the videos/DVD’s that fall into that category. The same should apply to the other prices mentioned above. “I have a couple of shop assistants that helps me out, and I would like some security built in so that the assistants cannot get access to my financial and other important data. Functionality: “I obviously need the system to take care of the most important part of the business:- the quick and accurate ‘booking out’ of all stock items. The customer, upon bringing me his/her selection, must be charged accordingly, and the items must be marked as ‘out’. “The system should also allow me to quickly and easily record the returned stock items, as and when they do come in. “Sometimes I also want to credit the customer for something, as the tape/DVD/game might have been damaged before they rented it. The item should then be marked as returned, but as money is then given back to the customer, some sort of record should be kept about this credit transaction so that I can trace which assistant allowed the credit. This will help me minimize fraudulent behaviour where assistants can basically book out resources ‘for free’. “I also want the system to have an advance booking facility, where an existing customer can call in and book a certain video/DVD/other item for a certain day. The system should not allow an item to be booked out twice for a certain date, and if something has been booked out and another customer tries to rent it, at least a warning should be displayed, informing the teller that this is the case. In special cases, such a booking can then be ignored, but most times the teller will inform the customer that s/he cannot have that item for the day. A facility should also be included where the booking can be cancelled at any time, if necessary. (For example, if a customer cancels the booking telephonically, whether it is on the day, or some time in advance). “Although it could be considered part of the accounting package, I would like this system to be able to do a daily summary, where I am presented with total sales (monetary value), total number of rentals (total videos; total DVD’s, total machines,) etc. This can be shown to me either on the screen, or in a printed form. I would like you to decide on the format and content of this screen/report. “Another function that I would like you to incorporate, is that the system should be able to do some analysis for me. Examples of this include: • Top Ten rentals • Top Ten customers • Stock items that have not been rented out in 6 months or more. I would like the above three to be done, but if you can think of other examples, feel free to add them in if you have time. “The system should allow me to add/edit all customer details, and if necessary (not often) delete a customer. Customer details to be stored include, but are not limited to: Name Surname Title I.D. Number Address and Postal Code Telephone Number (Work) Telephone Number (Home) Telephone Number (Mobile) “The system should also allow me to update the information regarding my stock items, for example: • Mark a tape as damaged. • Change a video from a ‘New Release’ to ‘Older Stock’. • Change the category it belongs to. “I have several working, but old machines lying around at my house, and they are already network-capable. I would like you to build some functionality where these machines can be linked to the system you are designing so that they can be used as ‘look-up’ machines. Basically, if a shop assistant is not available, but a customer knows the title of the movie they are looking for, they should be able to go to one of these terminals that I will set up throughout my shop, and enter or select the movie name, and perhaps what they are looking for (video/dvd/game etc). If my shop carries the chosen item, then the system should give them enough information (shelf number/category etc.) to be able to locate the item in the shop. It should also show if an item is unavailable, and when it is due back. If they select an invalid item, they should be informed of this. “The above program should run independently of the main system, and should not access the database directly. The video store will have employees, customers, stock and suppliers. Employees, customers and suppliers related to the video store can be created, deleted or updated. Creating / updating / deleting a customer profile (video store) will be very similar to that of creating / updating / deleting a customer’s account in the banking industry. The stock status also needs to be up to date (available, rented, late or damaged). An ATM will be inside the video store. The ATM is available to both the public and the employees. The ATM can be used for: Bank account balance inquiry, money withdrawal, funds transfer and transaction history (last 5 transactions with dates, time, type of transaction and outcome). The ATM should also cancel a transaction request and swallow a debit card when the user has entered a wrong pin number three times in succession. The ATM can only be used by clients who have existing bank accounts and existing (valid) debit cards. Make provision for situations such as expired debit cards, frozen accounts, insufficient funds, daily withdrawal limit exceeded, etc. The video store works on a cash-only-basis. Customers can withdraw money at the ATM if they don't have cash on them. The ATM is also available to public who only wants to use the ATM (without having to do business with the video store). Payment for stock rented: A Point Of Sale screen (electronic cash register screen) needs to be displayed. The product and the quantity thereof needs to be entered. You can make use of drop boxes if you want to. The system will calculate the total amount due (and the due date back for the products). Enter the cash amount offered by the customer. Calculate the change amount. Update the video store transaction register. Stock returned: Update the electronic system. Make provision for the condition in which the stock items were returned (in a working state or damaged, on time or late - individually). Capture a history record of products rented. Know the value of the stock outside the store. Capture a history record of products currently late. Capture a history record of products damaged. Capture a history record of products currently in store. Calculate the value of stock in-store. Capture a history record of each registered client's rental record. Capture a history record of a client's ATM transactions.

In developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]

This repository features an ETL pipeline for retrieving, processing, validating, and ingesting game metadata and sales data from SteamSpy and Steam APIs. Data is stored in a MySQL database on Aiven Cloud and visualized using Tableau dashboards for insightful analysis of gaming trends and sales performance.

A SQL-based data analysis project for a pizza store, built using CSV datasets. It explores sales performance, order patterns, and revenue insights through structured queries and joins.

A complete end-to-end MySQL project analyzing grocery store operations through database design, complex SQL queries, and business insights. Includes ERD modeling, customer & sales analysis, supplier performance evaluation, and data-driven recommendations for improving retail decision-making.

The process of analyzing raw data to find trends and insights can be used to make decisions that benefit anyone. In data analysis, we perform various operations like obtaining raw data, organizing and cleaning it, storing it in an accessible place, modeling, and then analyzing data to extract insights that support decision-making.We had to perform data analysis and visualization and develop a dashboard to help make data-driven decisions to increase the company's sales and revenue to complete the project. The objective was to analyze the sales and revenue trend and identify major markets from a sales and revenue perspective.

DESCRIPTION One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc. Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available. Dataset Description This is the historical data which covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields: Store - the store number Date - the week of sales Weekly_Sales - sales for the given store Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week Temperature - Temperature on the day of sale Fuel_Price - Cost of fuel in the region CPI – Prevailing consumer price index Unemployment - Prevailing unemployment rate Holiday Events Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13 Analysis Tasks Basic Statistics tasks Which store has maximum sales Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation Which store/s has good quarterly growth rate in Q3’2012 Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together Provide a monthly and semester view of sales in units and give insights Statistical Model For Store 1 – Build prediction models to forecast demand

Problem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.

'Vrinda Store' Data Analysis Project using Excel

A fully interactive Excel dashboard built using Super Store Sales data. Includes KPIs, regional insights, monthly trends, category analysis, and customer segmentation using Power Query, Pivot Tables, and advanced Excel visualization techniques.

We all eagerly wait for Black Friday sales and plan ahead in order to make most out of it. Similar is the objective of a retail outlet on Black Friday. They also aspire to bring the best out of this day. The major objective of a store is to maximize the revenue on this day, by selling off a large proportion of their unsold inventory. The main challenge to achieve this objective is “What optimal prices should the store set to capture demand that maximizes revenue?” The problem we solve would help the business to get the predicted Purchase amount (or Willingness to Pay) for each product for each user. They can use this then to set optimal prices on the product (using Multinomial Model for Price Optimization or others). So, when we find Black Friday Sales Analysis data on Kaggle, it highly motivated our team to work for this interesting real-world problem for ABC Retail Store.

For More Details please contact Call / Whatsapp: + 91 9840566115 Website: www.mlmappfactory.com 26, 49th Avenue, Ashok Nagar, Chennai – 600087 Solana Token Development Company We at MLM App Factory help start-ups and enterprises launch user-friendly decentralized applications powered by the scalability and speed of the Solana network. Our Solana blockchain development services cater to a range of projects spanning DeFi, Web3 and NFTs. Our Solana Blockchain Development Services Solana Blockchain Consulting Our consultancy services help you understand the prospect of Solana blockchain development for your business through strategic and technical analysis. It enables you to better leverage the speed and scalability of the Solana blockchain across a range of projects. DApp Development We help you launch scalable and user-friendly dApps on the Solana blockchain. We design and build dApps related to Payment, Token swap, Peer2Peer lending, NFT marketplaces, Stable coin and many more. NFT Marketplace Development We help you build and deploy your own NFT Marketplace on Solana Blockchain. Our development services optimize your marketplace for decentralized NFT minting, storefronts, sales and other marketplace features. SPL Token Development We help you tokenize your assets by creating new SPL tokens for them. These tokens are exchangeable on decentralized exchanges, useful for investment purpose and powers Solana dApps. Defi Development To help you tap the potential of Defi and appeal the worldwide borrowers and lenders, we build a range of Defi systems such as P2P decentralized lending platforms and Crypto loan platforms on top of Solana blockchain. Why Choose MLM App Factory for Solana Development? Wide Experience of Projects We have conceptualized, built and delivered 1000+ digital solutions and 62+ robust blockchain projects and deployed 80+ smart contracts. Impressive Work Portfolio From building supply-chain and monetary systems on blockchain to creating ready-to-deploy NFT solutions, we showcase an impressive work-portfolio. One-stop Blockchain Services Whether you are looking for developing a massive Defi or NFT marketplace project or a simple wallet development solution, we are the one-stop destination for all kinds of blockchain development services. Long-term Collaborations We build scalable relationships with our clients. Through market analysis, research and development, we continue building business and technical strategies to help our clients further scale up their projects. Benefits of using Solana Blockchain Solana makes it possible for a centralized database to process 710,000 transactions per second on a standard gigabit network. The transaction fee is less than $0.01 for users and developers Solana becomes the fastest because of its 400 millisecond block times Clock verification makes Solana unique Node synchronization makes the transactions fast. Proof of History helps in integrating timestamp with every transaction approval following to track the transactions which serve as a clock. What technology stack does Solana use? Proof-of-History A Clock Before Consensus because nodes in a distributed network can’t trust the timestamp on messages received from other nodes, the biggest problem in distributed networks is agreeing on the time and order in which events happen. Turbine Solana uses a distinct but linked protocol called Turbine to transmit blocks (communicate blocks between validators) independent of consensus. The Turbine is primarily influenced by Bit Torrent and is designed for streaming. Tower BFT A PoH-optimized version of PBFT Solana runs a consensus mechanism dubbed Tower BFT on top of Proof of History, which is a PBFT-like algorithm that uses the synchronized clock allowed by PoH to reach consensus on network transactions. Pipeline A Transaction Processing Unit for validation optimization on the Solana network, the transaction validation procedure takes full use of pipelining, a CPU design improvement. When there is a stream of incoming data that has to be processed in a series of steps with distinct hardware accountable for each step, pipelining is an acceptable technique. Archives Distributed ledger storage The use of a high-performance network to store and maintain data is expected to become a key centralization vector.

Data Analysis of super store sales records

Focuses on analyzing Walmart's sales data. It involves cleaning and transforming the dataset, calculating total sales, and storing the cleaned data in a MySQL database. The goal is to prepare the data for further analysis and insights.

This project revived a dormant laptop store using advanced data analysis, Python tools like BeautifulSoup and scikit-learn, and a robust MySQL/SQL Server database. We created interactive visualizations with Power BI and Streamlit, empowering effective sales strategies through machine learning-driven insights and comprehensive data analysis.

Assignment About the data: Let’s consider a Company dataset with around 10 variables and 400 records. The attributes are as follows: Sales -- Unit sales (in thousands) at each location Competitor Price -- Price charged by competitor at each location Income -- Community income level (in thousands of dollars) Advertising -- Local advertising budget for company at each location (in thousands of dollars) Population -- Population size in region (in thousands) Price -- Price company charges for car seats at each site Shelf Location at stores -- A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site Age -- Average age of the local population Education -- Education level at each location Urban -- A factor with levels No and Yes to indicate whether the store is in an urban or rural location US -- A factor with levels No and Yes to indicate whether the store is in the US or not The company dataset looks like this: Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.

The Super Sales Store project leverages data analysis, emphasizing time series, for insights and precise sales forecasts, enhancing success.

Analysis the data using Excel and gaining insight form it to improve the sales of store

A MapleStory auction house companion to scrap historical sales data and store into a mySQL database for further statistical analysis.

This repository contains the code and data for analyzing pizza sales using SQL Server as the data store and Power BI for data visualization and analysis. Explore sales trends, customer preferences, and more to gain insights into pizza sales using this repository.

Sales Analysis of electronics store purchases of 12 months worth of data using Python Pandas and Python Matplotlib to answer business questions.

Coffee Shop Sales Analysis Dashboard built in Microsoft Excel using Power Query, Pivot Tables, and Visualizations. This project uncovers insights from real-world coffee shop data including peak hours, sales distribution, top-selling products, store footfall, and more

Target Store Sales Prediction – Objective& Deliverables Content: You are provided with historical sales data for 45 stores located in different region search store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Objective & Deliverables Problem description: One challenge of modeling retail data is the need to make decisions based on limited history. Holidays and select major events come once a year, and so does the chance to see how strategic decisions impacted the bottom line. In addition, markdowns are known to affect sales the challenge is to predict which departments will be affected and to what extent. Recommended Project Steps & Guidelines: 1. Understand the data variables properly. Check the variable description to understand the data properly. 2. Clean the data: Clean the data, that is, fill the missing values (if any), treat the outliers (or odd values), etc. Ensure each variable’s data is as per the nature of the variable (e.g. – Date field should contain only date values – can extract year, month and day of the week, and numeric column should be formatted as numeric, etc.). 3. Conduct EDA (Exploratory Data Analysis) on the cleaned Data: Summarize, explore the data and then decide your strategy. Make note of any important assumptions that you make. 4. Uni-variate and Bi-variate Analysis: Check the distribution of independent variables and also compare them with the dependent variable. 5. Feature Engineering: Create new meaningful features based on the existing features by applying some aggregation functions on them. 6. Hypothesis Testing: Hypothesis testing in statistics is a way for you to test the results of a survey or experiment to see if you have meaningful results. You should give a brief summary of the data and a summary of the results of your statistical test. In the discussion, you can discuss whether your initial hypothesis was supported or refuted. TARGET STORE SALES PREDICTION 7. Identify the most important variables (or data parameters) that affect the final decision: Identify the impact of each variable on the final result graphically (correlation / scatter plots, regression plots, etc.). Keep those variables that affect the final outcome. 8. Develop and Validate Samples: Divide samples into 2 parts: Development Sample (70%) & Validation Sample (30%). Build your analysis model using the Development Sample, and validate it on the validation sample and then predict on test sample. 9. Model Building: Analyze the dependent variable and decide which technique out of regression or classification to use and hence build the model. 10. Improving model accuracy: We know that machine learning algorithms are driven by parameters. These parameters majorly influence the outcome of learning process. So, find the optimum value for each parameter to improve the accuracy of the model and repeat this process with a number of well performing models. 11. Model Comparison: Comparing the each model with other similar models and then choose that model which give highest accuracy. But it is not necessary that higher accuracy models always perform better (for unseen data points). So, find the right accuracy of the model, you must use cross validation technique before finalizing the model.

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Data Visualization and Analytics on a real-world dataset obtained from local medical store.

This project showcases a complete data analysis workflow using Python and the OSEMN framework on a pet store sales dataset. It covers data cleaning, exploratory analysis, and visualization to extract meaningful business insights. The repository demonstrates practical application of Python libraries like Pandas and Seaborn.