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Pizza Bay Management System

Slice your way to success with Pizza Bay Management System – the ultimate tool for streamlining your pizza restaurant operations!

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Pizza Bay Management System is a SQL-based management project designed for pizza restaurants to efficiently manage their day-to-day operations. The system is built to simplify the entire process from order-taking to inventory management and employee scheduling.

The system allows users to create and manage customer orders, which includes the ability to select pizza toppings and other food items, specify delivery or pickup, and process payments. The system also tracks order status and provides real-time updates to customers.

Inventory management is an important part of any restaurant, and Pizza Bay Management System provides the ability to track inventory levels, set alerts for low stock, and automatically reorder items when necessary.

Employee scheduling is made easy with the system’s intuitive scheduling tool, which allows managers to create and manage employee schedules, assign tasks, and track employee hours.

The Pizza Bay Management System also provides powerful reporting capabilities, allowing managers to track sales, revenue, and inventory levels over time. The system’s customizable dashboards and reporting tools provide managers with real-time insights into their business performance.

Overall, the Pizza Bay Management System is an all-in-one solution for pizza restaurant management, helping to increase efficiency, reduce costs, and improve customer satisfaction.

Most recent Debt/Equity Ratio greater than the median industry average: High debt/equity ratio implies high leverage. High leverage indicates a huge level of repayment that the company has to make in connection with the debt amount.

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Fitness Tracker Market Analysis

Get in shape for success with Fitness Tracker Market Analysis – your guide to staying ahead of the competition!

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– Understanding Buying Patterns and Price Drivers in India

As a fitness enthusiast, I embarked on a project to analyze the buying pattern for fitness trackers in the Indian market and determine what factors drive their prices. My aim was to gain insight into the Indian fitness band buying population and help companies understand the variables that affect the overall sales of Fitness bands.

To achieve my goal, I gathered my dataset from Kaggle.com, which I had originally collected from an e-commerce website Flipkart. I was confident that my sample accurately represents the Indian fitness band buying population as it was randomly selected.

After analyzing the dataset, I found that the selling price of Fitness watches is based on various variables like Original Price, Rating, Average Battery life, and FitnessBand. I observed that the average cost for Apple products was the highest, followed by Garmin and Samsung. However, Apple had the least battery life, while Garmin had the most battery life.

In the second part of my project, I aimed to determine how various factors like the Original Price, Display, Average battery life, Rating, Color, and Device type affect the selling price (dependent variable) for a Fitness watch. To achieve this, I developed a regression model with maximum predictive power to evaluate the relationship between the dependent and explanatory variables.

I used scatter plots to determine the association between the X and Y variables. My dependent variable was the Selling Price, and the explanatory variables were the Original Price, Rating, Average battery life, Display, Color, and Device type. I assumed the Original price to have a linear relationship with Selling Price because Selling Price is obtained from Original price. I calculated dummy variables for Color, Device type, and Display to convert categorical variables into numerical variables.

I selected Average Battery Life and Rating to predict their effect on Selling Price. Based on the regression analysis, I concluded that using the equation for Predicted Selling price, I could accurately predict the selling price of a Fitness Band with 96.7% accuracy.

My project’s findings will help companies that produce fitness products effectively predict the selling prices of their products and understand how variables like average battery life, original price, ratings, Display, Color, and Device type affect the overall sales of Fitness bands.

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Suicide Rate Prediction.

Prevent tragedy with precision – Suicide Rate Prediction is the tool for saving lives.

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In the Suicide Rate Prediction project, the descriptive analysis of the dataset was performed to identify the distribution of the features, their relationship with each other, and the potential outliers.

Feature engineering was conducted to prepare the dataset for the machine learning models. This involved data cleaning, normalization, scaling, and selection of the most relevant features for the models.

Several machine learning algorithms such as Decision Tree, Random Forest, and Support Vector Machine were used to predict the risk of suicide. The performance of each model was evaluated using various metrics such as accuracy, precision, recall, and F1-score. The best-performing model – Random Forest was selected and its hyperparameters were optimized to improve the performance further.

The results of the machine learning models were analyzed to identify the factors that are most strongly associated with the risk of suicide. The feature importance of each attribute was evaluated, and their impact on the prediction of suicide risk was interpreted. Furthermore, the geographical distribution of suicide risk was visualized using various maps and plots.

The results were interpreted to gain a better understanding of the underlying causes of the increase in suicide rates around the globe.

In conclusion, this project aimed to forecast the risk of suicide and analyze the associated factors responsible for the rise in suicide rates globally. The machine learning models were trained to classify the suicide risk, and the results were interpreted to identify the most significant factors affecting the prediction. The project provided valuable insights that can be used to develop effective prevention strategies.

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Dominick Data Analysis

Unlock the power of data and dominate your market with Dominick Data Analysis

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The Dominick Data Analysis project aimed to examine the correlation between demand (logmove) and various factors such as price, brand, feat, and demographic variables across different store locations, using Dominick’s orange juice sales database. The dataset included 5000 cases and attributes such as storeweek, UPC, store, move, logmove, quantity, price, logprice, profit, and feat were selected for the analysis.

The project used pivot tables to select two low-priced brands (HH and TREE FRESH) and one high-priced brand (TROPICANA PURE PREM) based on their average prices, and regression models and hypothesis tests were conducted to answer research questions. The results of the analysis suggested that price elasticity of demand varies across different customer segments, indicating that businesses should adopt different pricing strategies for different customer groups to maximize revenue. The lack of significant difference in demand between spring and summer suggested that businesses may not need to make significant adjustments to their operations during this time.

The project’s findings also suggested that businesses should use seasonality analysis to optimize inventory and staffing levels to meet demand during peak periods and minimize waste during slow periods. Furthermore, by dropping demographic variables with higher p-values, businesses can focus on the most relevant factors that impact demand. Knowing the price elasticity of demand for different customer segments can help businesses set optimal prices for each group, maximizing revenue. The significant relationships between demand and certain demographic variables (EDUC, SINGLE, WORKWOM, AGE60) can also help businesses better target their marketing efforts and tailor their products/services to meet the needs and preferences of specific customer segments.

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Amazon Recommender System.

Efficiently Managed Amazon Recommender System.

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The primary objective of the Amazon Recommender System database management project was to evaluate customers’ purchasing behaviors on Amazon and recommend related products to increase profits. The project aimed to solve numerous business issues, such as identifying the top paying customers in December, determining the most popular payment method, and identifying the sub-category with the highest sales during the same month.

Moreover, the project aimed to develop a recommender system based on collaborative filtering and purchase data to suggest the most relevant products to a specific customer based on their previous orders. A dataset containing customer information, order information, and payment information was employed for the project. Seven tables, including customers, orders, payments, main-categories, sub-categories, recommender system, and order-subcategory, were created to model the dataset.

The tables were linked using primary keys, and entity relationship diagrams were used to visualize the database structure. The project encountered issues such as authorization limitations and the need to modify data models multiple times during the query process due to unfamiliarity with the recommender system and its functions. Despite these challenges, the project provided opportunities to perform several database management tasks, including CREATE/DROP table, INSERT values, and use SELECT statements to analyze data.

The project laid the groundwork for future recommender system development, and the knowledge gained from it can be used to improve future projects and professional experience. Overall, the project highlights the importance of effective recommender systems in today’s digital world and the potential of data-driven insights for businesses.

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Resort Booking Data Analysis.

Maximize your bookings and minimize cancellations with Resort Booking Analysis –the key to unlocking your hotel’s full potential!

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The Resort Booking Analysis project aimed to enhance the customer experience by identifying patterns in customer behavior and preferences. By analyzing data on customer requests, preferences, and complaints, hotel managers could gain a better understanding of their guests and tailor their services accordingly. The project also explored the relationship between customer satisfaction and booking cancellations, highlighting the importance of providing excellent customer service to reduce cancellations.

The predictive modeling and machine learning techniques used in the project helped to develop a reliable booking cancellation prediction model, which could be used to forecast the likelihood of a booking being canceled. This allowed hotel managers to take proactive measures, such as overbooking or offering incentives to guests to prevent cancellations, thereby reducing lost revenue.

The insights provided by the Resort Booking Analysis project could also be used to develop targeted marketing campaigns to attract specific customer segments and increase bookings. By analyzing customer data, hotel managers could identify patterns in booking behavior and preferences, allowing them to tailor their marketing efforts to specific groups.

Overall, the Resort Booking Analysis project provided valuable insights and practical solutions to help hotel managers in the resort industry reduce booking cancellations, enhance the customer experience, and improve their overall sales and reputation. By leveraging data-driven approaches, hotel managers can stay ahead of the competition and provide the best possible experience for their guests.