Restaurant Management System ensures that the common inconvenience faced by people is solved. The systematic user interface of the website F2 - Food n Fun is ease of access to the users which entices the customers. As general public, all of us use online platforms to order food, book tables or opt for food delivery and catering services as per our needs. Many restaurants are not known to public and our platform paves a way to reach almost all restaurants. Hence, our system targets the general public and the restaurants around. The end-users are Public and Restaurant Managers.

Analyzing campus placement data is a crucial task that provides a comprehensive understanding of students' academic performance, skill sets, internships, and how these factors contribute to their ultimate placement outcomes. By employing machine learning techniques to delve into this wealth of information, valuable insights can be extracted to discern the key determinants of placement success and devise effective strategies for enhancing the overall placement process.
The proposed solution aims to utilize machine learning techniques to analyze campus placement data and extract valuable insights. The dataset will contain information about students, their Academic Records, Work Experience, Employability Test Percentage, Post Graduation - Specialization, and their eventual Placement outcomes. By identifying patterns and trends within this data, colleges and universities can gain a better understanding of the factors influencing placement success and take measures to improve the overall placement process.

Steps Involved

• Data Pre-processing
• Exploratory Data Analysis (EDA)
• Classification Model
• Performance Testing
• Deployment

Functional Specifications

• User Interface: Html, CSS - Bootstrap, JavaScript
• Middleware: Python - Flask framework
• ML Model:
• Random Forest Classifier after performing Hyper-parameter Tuning with Randomized Search CV gives 90% best score
• Linear Regressor with R2 Score of 0.01742145919589766
• Deployment: IBM Watson Cloud

Cancer is a complex and multifaceted disease that can have a profound impact on individuals, families, and communities. In order to understand the scope and impact of cancer, it is important to track key measures such as cancer mortality and incidence rates. These measures provide important insights into the prevalence and impact of cancer within a given population, as well as how this burden is changing over time. The causes of Cancer mortality and incidence rates are complex and multifactorial, involving a combination of genetic, environmental, and lifestyle factors.
Machine learning algorithms are trained on large datasets of cancer-related data, including patient demographics, medical histories, genetic data, and other relevant factors, to identify patterns and predict cancer mortality and incidence rates. The goal of this task is to predict status of cancer incidence or mortality rate based on a set of features.

Steps Involved

• Data Pre-processing
• Exploratory Data Analysis (EDA)
• Classification Model
• Performance Testing
• Deployment

Functional Specifications

• User Interface: Html, CSS - Bootstrap, JavaScript
• Middleware: Python - Flask framework
• ML Model: XGBoost Classifier after performing Hyper-parameter Tuning with Randomized Search CV gives 98.21996% accuracy score
• Deployment: IBM Watson Cloud

Shopping outlets like Fresh Grocer keeps track of individual items sales data in order to forecast future client demand and adjust inventory management. In a data warehouse, these data stores hold a significant amount of consumer information and particular item details.
By mining the data from the data warehouse, more anomalies and common patterns are discovered. This project predicts the sales of the different stores of Fresh Grocer based on various characteristics.

Steps Involved

• Data Pre-processing
• Exploratory Data Analysis (EDA)
• Classification Model
• Performance Testing
• Deployment

Functional Specifications

• User Interface: Html, CSS - Bootstrap
• Middleware: Python - Flask framework
• ML Model: Random Forest Regressor after performing Hyper-parameter Tuning with Grid Search CV gives 81.56% accuracy score
• Deployment: Joblib Library

Common problem in the real estate industry is the House Price Prediction. The goal is to predict the cost price of a house based on various features and attributes. It is approached as a regression problem, where the target variable is the price of the house, and the features include quantitative and categorical variables such as the area of the house, number of bedrooms and bathrooms, number of stories, proximity to the main road, presence of a guest room, basement, hot water facility, air conditioning, parking, and furnishing status.
Accurate house price predictions can be valuable for various stakeholders in the real estate industry. Real estate agents and appraisers can use the predictions to price homes correctly, while house owners can set a reasonable asking price for their properties. Buyers can make informed decisions and negotiate fair prices for their investments.

Steps Involved

• Data Pre-processing
• Exploratory Data Analysis (EDA)
• Classification Model
• Performance Testing
• Deployment

Functional Specifications

• User Interface: Html, CSS - Bootstrap, JavaScript
• Middleware: Python - Flask framework
• ML Model: Linear Regressor after performing Hyper-parameter Tuning with Grid Search CV with 86.598% accuracy score
• Deployment: IBM Watson Cloud