This study aims to provide a detailed analysis of Gurgaon's real estate sector. Gurgaon has emerged as a prime real estate destination for the next decade, surpassing neighbouring cities New Delhi and Noida in terms of property prices. In this study we analyse the market trends, infrastructural developments, and economic factors, in order to show the potential for returns on investment for property owners. It also identifies key areas and upcoming project launches that promise high returns.
The other facet of this study is to provide a detailed guide on the essential considerations for prospective homeowners in Gurgaon. This section offers practical advice on factors such as interior design ideas, location, connectivity, amenities, and legal aspects of home ownership. This will aid potential buyers with the knowledge needed to make informed decisions.
The real estate sector of India is witnessing rapid growth. Not only does it provide housing and commercial spaces to people, but it is also accepted as a source of income for many. There are multiple ways in which a buyer chooses to utilise real estate. For instance, they could hold the purchased property to resell it at a higher price or rent out the real estate for additional income. Another means of gain is of converting the available real estate into a homestay or hotel. Although each has its own benefits, a buyer seeks to make the optimal choice to maximise the returns on his investment.
There are many factors which affect the prices and trends in real estate for a dynamic market like NCR (National Capital Region). Preliminary analysis shows us that there are three main categories of factors: demographic, socioeconomic, and infrastructure. The goals of this study are to identify them and establish the linkage between the factors and the prices of property. This purpose of this study is to help property buyers foresee trends in real estate and make optimal investment decisions.
Businesses serve as the backbone of the economy yet all of them bear the persistent challenge of deriving profitability which jeopardises their long-term viability. In FY 22, about 10 startup unicorns witnessed huge losses with the highest-making unicorn being Bharatpe with a $726 million loss. Some other unicorns were Flipkart, Meesho, Swiggy, Unacademy, Paytm, Udaan, Phonepe, verse, and Sharechat. The key issue which led to this profitability problem was the lack of insights available for assessing the customer retention rate, costs, product differentiation status, or the medium providing sales to the businesses. Apart from these, factors like lack of cash flow management, less knowledge of technology, operational inefficiencies, and lack of ability to manage business also hinder businesses profitability prospect. Among these issues, a lack of focus on market dynamics emerges as the major concern for businesses.
This module focuses on the case of Bigbasket company. With millions of consumers relying on the company for daily essentials, the need of Bigbasket has been constantly managing its inventory effectively. As there are many perishable products included in the product line, the procedure of managing inventory becomes more complex and challenging. The process becomes more complicated due to a lack of infrastructure availability for preserving food. Poor inventory management results in many a time having out-of-stock situations, overstocking of less demanded products, and delivery of expired or damaged products to the customer. This results in the failure of the company to secure consumer demand timely and remain competitive in the market.
The objective of this module is to address the issue of poor inventory management by assessing consumer demand patterns and making relevant predictions about the change in demand. The study will use basic and advanced Excel as the tool of analysis for fulfilling the two-fold objective of the case. For this, five datasets were considered:
We use a mix of business market analysis, descriptive analysis, financial statement analysis, regression analysis, sensitivity analysis, and optimization to recommend for empowering businesses to thrive in a competitive business market.
Money laundering is a multi-billion-dollar issue. Detection of laundering is very difficult. Banks and regulatory authorities struggle to identify these illegal transactions. Not only does it cost them billions in unpaid taxes, but it also promotes crime at the cost of socioeconomic health of a country.
There exist many automated algorithms which aim to detect illegal transactions but most of them have a high false positive rate: legitimate transactions are incorrectly flagged as laundering. The converse is also a major problem --false negatives, i.e. undetected laundering transactions. Naturally, criminals work hard to cover their tracks.
The aim of this module is to utilise IBM’s synthetic dataset of over 10 lakh financial transactions to create a deep learning model for fraud detection. The dataset, which is available on the Kaggle repository, is based on a virtual world inhabited by individuals, companies, and banks. Individuals interact with other individuals and companies. Likewise, companies interact with other companies and with individuals. These interactions can take many forms, e.g. purchase of consumer goods and services, purchase orders for industrial supplies, payment of salaries, repayment of loans, and more. These financial transactions are generally conducted via banks. Using a combination of supervised learning, deep learning, and GridSearchCV assisted models, this module will aim to achieve at least 90% accuracy in identifying illegal transactions.