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.

Sales Conversion Optimization MLOps: Boost revenue with AI-powered insights. Features H2O AutoML, ZenML pipelines, Neptune.ai tracking, data validation, drift analysis, CI/CD, Streamlit app, Docker, and GitHub Actions. Includes e-mail alerts, Discord/Slack integration, and SHAP interpretability. Streamline ML workflow and enhance sales performance.

Superstore Sales with Streamlit is a data visualization and analysis project that uses the Streamlit framework to create an interactive web application for exploring and analyzing sales data from a superstore. This project aims to provide an easy-to-use interface for users to gain insights into sales trends, Sales performance, product performance,

Develop a data science project using historical sales data to build a regression model that accurately predicts future sales. Preprocess the dataset, conduct exploratory analysis, select relevant features, and employ regression algorithms for model development. Evaluate model performance, optimize hyperparameters, and provide actionable insights.

This Power BI dashboard project offers a comprehensive analysis of Blinkit, India's Last Minute App, focusing on sales performance, customer satisfaction, and inventory distribution. The interactive dashboard transforms raw data into actionable insights, enabling data-driven decision-making for business optimization.

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✨Excited to share my Excel-based Sales Performance Dashboard, built as part of my Data Science minor project! It showcases key metrics like revenue trends, top products & salesperson analysis. Check out the video & feel free to share your feedback!

Utilizing a combination of Excel, SQL, and Power BI, I delved into an extensive dataset comprising over 50,000 entries of pizza sales data from our business. Through meticulous data simplification and analysis, I uncovered compelling insights and illuminating trends about sales performance and customer preferences within the pizza market.

An Excel-based sales and customer analysis dashboard designed for a fashion e-commerce store. The project visualizes key business insights including monthly sales trends, product category performance, customer demographics, retention analysis, and regional market reach.

This repository contains an Exploratory Data Analysis (EDA) project performed on a sales dataset using Python. The objective is to extract meaningful insights and visualize sales performance across time, geography, customer segments, and discount strategies.

This project offers an in-depth analysis of SuperStore's sales data, aiming to uncover actionable insights into customer behavior, regional performance, product trends, and shipping efficiency. By leveraging interactive visualizations and time series forecasting, the dashboard serves as a strategic tool for data-driven decision-making.

This project provides a comprehensive analysis of coffee shop sales data, focusing on customer purchasing patterns, product performance, and revenue generation over a specified time period. Developed using Microsoft Excel

This repository presents an end-to-end data analysis project focused on simulating real-world business intelligence workflows. Using historical sales and returns data, the analysis identifies key performance drivers across regions, categories, and customer types, offering actionable insights for business decision-making.

No description available

- Analyzed Walmart sales data from three branches using SQL and Python, focusing on product performance, sales trends, and customer behavior to optimize sales strategies and identify key performance factors. - Performed data wrangling, feature engineering, and exploratory data analysis to uncover insights

This repository contains a Jupyter Notebook (Walmart Analysis.ipynb) that analyzes Walmart's sales data, focusing on identifying trends, patterns, and actionable insights. The analysis aims to understand sales performance and improve business strategies.

! 🛒This project is a comprehensive E-Commerce Sales Analysis Dashboard built using Power BI, designed to help stakeholders monitor performance, identify trends, and make data-driven business decisions.

This repository features the Superstore Sales Analysis project, demonstrating data cleaning and analysis using Python and SQL, along with interactive visualization in Power BI. Explore the code and reports to gain insights into sales performance, customer behavior, and product analysis.

The aim of this project is to provide a comprehensive analysis of the sales performance of the beverage industry by examining key factors such as product categories, supplier contributions, customer demand, and sales trends over time.

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 data analytics project using Python and Jupyter that explores and visualizes sales performance. Includes data cleaning, trend analysis, and meaningful business insights with charts and reports. Ideal for showcasing real-world data analysis skills.

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.

Developing an automated system for trend analysis in e-commerce. Utilizing Xpath and Python to obtain data on top-selling products from influential marketplaces like Amazon and eBay based on user choice. Using AI to transform data into visualized insights, aiding business owners in product selection decisions and enhancing sales performance.

Interactive Sales Analysis Dashboard built using powerBi to analyze sales performance through visualizations and insights.

Performed data cleaning, visualization, and analysis on supermarket sales data using Python libraries. Identified sales trends, customer behavior patterns, and product performance insights

End-to-End Data Pipeline for Sales Analysis: This project showcases a data pipeline using Snowflake, dbt, and Tableau to transform raw sales data into structured insights. It employs incremental data processing for optimized performance and features a star schema design for efficient querying and reporting.

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.

This is a project that shows my ability to query data from Adventure Works database Using SQL, thereafter using PowerBI to import , analyze and visualise.

Maven Toys, a toy company is looking to expand their business with new stores. This is a sales performance analysis focused on analyzing interesting patterns and trends in their data to help them make informed decisions.