IMPACT OF CLIMATE CHANGE IN THE PROPERTY PRICES OF UK.
An internship report submitted in partial fulfilment of requirements for
Masters of Business Administration (Business Analytics)
1 Month 15 days
Under the guidance of:
                    	                                                                                                                                                                                                                                            

                    Internal Mentor :                                                                                  External Mentors :
                    Mr. Neeraj Rathore                                                                              Mr Pranav Dar                                                                 
                    Professor  (UPES)                                                                               Manager (KPMG)
                                                                                                                                    
                                                                                                                                    
                                   



Submitted by
Rohit Kothari
SAP id: 500117908
Enrolment Number: R116223016
MBA (Business Analytics)
2023-2025
School of Business, UPES


STUDENT DECLARATION



I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.



                                                                                                                










Rohit Kothari
Sap Id: 500117908
Enrolment Number: R116223016
MBA Business Analytics
2nd Year
School of Business, UPES							 


ACKNOWLEDGEMENT


I express my sincere gratitude to all who supported me during my internship at KPMG India (Digital Lighthouse) . I am deeply indebted to my mentors, Mr Pranav Dar, Manager Lighthouse), KPMG India, for their expert guidance, and PROFF. NEERAJ RATHORE UPES Dehradun, for their continuous academic support.
I would also like to express my gratitude convey my deepest regards to Mr <PRESIDIO_ANONYMIZED_PERSON>, Partner (KPMG) for his constant support and encouragement that helped considerably in the successful completion of this project. 
 I would like to convey my deepest regards to my manager Mr Ravi Sahu, Manager (KPMG India) for his exemplary guidance, monitoring and constant encouragement throughout the course of this project. His constant motivation and technical support helped considerably in the successful completion of this project. 
 It was indeed a very enriching experience where I got to work on a challenging project with some of the hardest working and talented people. The experiences I had KPMG India will stay with me forever. I perceive as this opportunity as a big milestone in my career development. I will strive to use gained skills and knowledge in the best possible way and I will continue to work on their improvement, in order to attain desired career objectives. 
 Hope to continue cooperation with all of you in the future


 

CERTIFICATE OF INDUSTRY GUIDE


This is to certify that the summer internship report entitled Impact of climate change in the property prices of UK submitted by Rohit Kothari to UPES for partial fulfilment of requirements for Master of Business Administration (Business Analytics) is a Bonafide record of the internship work carried out by her/him under my (our) supervision and guidance. The content of the report, in full or parts, has not been submitted to any other Institute or University for the award of any other degree or diploma.




                
                                                                                                                   
Mentor:  

                                                                                                                                 
Mr Pranav Dar 
Manager 
Digital lighthouse (KPMG India)

 

CERTIFICATE OF FACULTY GUIDE



This is to certify that the summer internship report entitled IMPACT OF CLIMATE CHANGE IN THE PROPERTY PRICES OF UK submitted by Rohit Kothari to UPES for partial fulfilment of requirements for Master of Business Administration (Business Analytics) is a Bonafide record of the internship work carried out by her/him under my (our) supervision and guidance. The content of the report, in full or parts, has not been submitted to any other Institute or University for the award of any other degree or diploma.








Mr. Neeraj Rathore 
Professor 
UPES (Dehradun)



TABLE OF CONTENT

1.	STUDENT DECLARATION……………………………………………3
2.	ACKNOWLEDGEMENT……………………………………………….4
3.	CERTIFICATE OF INDUSTRY GUIDE……………………………....5
4.	CERTIFICATE OF FACULTY GUIDE………………………………...6
5.	EXECUTIVE SUMMARY………………………………………………8
6.	INTRODUCTION……………………………………………………......10
7.	RESEARCH METHODOLOGY………………………………………12
8.	INTERNSHIP EXPERIENCE………………………………………....21
9.	CONCLUSION………………………………………………………....23
10.	RECOMMENDATION AND SUGGESTION………………………...24
11.	  BIBLIOGRAPHY……………………………………………………….26






EXECUTIVE SUMMARY



Title – Impact of Climate Change on Property Prices in the UK

During my internship at KPMG India, I had the opportunity to work in the Digital Lighthouse department under the mentorship of Pranav Dar and guidance of Mr. Ravi Sahu. I actively contributed to a range of projects centred around data analytics, predictive modelling, and research. Below is an overview of the major projects I worked on and the skills I developed during this internship.

Climate Change and Property Prices Prediction Model
One of the key projects I contributed to involved developing a model to predict how climate change could affect property prices in the UK. This required extensive data collection from multiple online sources using web scraping techniques and conducting thorough secondary research. We employed advanced Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA), to analyse patterns and derive critical insights from the data. The final stage involved building a regression model to predict future property price trends based on climate factors. The insights provided by this model are valuable for real estate stakeholders in understanding the long-term risks and opportunities posed by environmental changes.

Through this project, I not only expanded my understanding of machine learning and NLP but also gained deeper insights into how external environmental factors like climate change can significantly impact economic sectors such as housing.

Marketing Data Analysis
I also contributed to analysing marketing data for a client project. This task involved cleaning and processing large datasets, followed by in-depth analysis to derive actionable insights. I identified key trends in customer behaviour, sales metrics, and campaign performance, which were used to formulate recommendations for optimizing the client’s marketing strategies. These insights helped the client allocate resources more effectively and maximize returns on marketing investments.

This project sharpened my data analysis skills, equipping me to handle and visualize large datasets while transforming raw data into practical business insights and strategies.

Research and Presentation Development
In addition to technical data work, I also played a key role in conducting research and creating client-facing presentations. My responsibilities included gathering and analyzing data, performing market and competitor research, and converting these findings into clear, well-structured presentations. These presentations were instrumental in guiding the client’s business decisions and strategy development. I was also involved in the preparation of reports and project deliverables, ensuring that the insights and recommendations were communicated effectively to senior stakeholders.

Through these tasks, I gained significant experience in using data visualization tools and enhanced my ability to translate complex data into impactful, decision-driven insights. My work also contributed to strengthening KPMG’s data management and reporting capabilities.

Key Learnings and Impact
During my internship, I gained practical experience in advanced analytics, predictive modeling, and data visualization. I developed a solid foundation in how to tackle complex business problems through data-driven approaches. My involvement in a wide range of projects allowed me to refine both my technical and communication skills. This hands-on experience has prepared me for future roles in the areas of data science, business intelligence, and consulting.

By the end of the internship, I had acquired a strong grasp of key concepts in machine learning, data analysis, and client engagement, and my contributions helped enhance the outcomes of various projects at KPMG..


















INTRODUCTION


KPMG India is a premier provider of professional services in the country, recognized for its extensive expertise in audit, tax, and advisory solutions. As part of the global KPMG network, which spans over 143 countries worldwide, KPMG India utilizes a vast knowledge base, coupled with deep industry experience, to deliver customized solutions tailored to the unique needs of businesses across multiple industries. These include key sectors such as financial services, healthcare, manufacturing, technology, infrastructure, and consumer markets.
KPMG India offers forward-thinking and strategic solutions that help businesses navigate critical challenges like digital transformation, regulatory compliance, risk management, and sustainability. With a sharp focus on technology and innovation, the firm leverages state-of-the-art tools, frameworks, and analytics to provide insights and strategies that empower organizations to thrive in an increasingly complex and rapidly evolving global marketplace.
A major strength of KPMG India is its commitment to maintaining the highest standards of quality and integrity. By fostering a client-centric approach, the firm has solidified its reputation as a trusted advisor in the consulting space. Whether guiding businesses through mergers and acquisitions, regulatory reforms, or operational transformations, KPMG India is known for delivering results that are sustainable and aligned with client goals.
Moreover, KPMG India emphasizes its role in corporate social responsibility, actively engaging in initiatives that support education, community development, and environmental sustainability. The firm's dedication to societal impact reflects its vision of contributing to the broader well-being of communities, beyond just its client services.
KPMG India’s strength lies in its highly skilled workforce, composed of seasoned professionals with diverse expertise ranging from data analytics and digital strategy to financial advisory and risk management. This depth of knowledge allows the firm to be a strategic partner for businesses, helping them drive growth, improve operational efficiency, and navigate an increasingly competitive business environment.
By providing businesses with the insights and strategies necessary to adapt to change and capitalize on opportunities, KPMG India continues to play a vital role in shaping the future of enterprises across various sectors, ensuring long-term success and sustainability.

Digital Lighthouse (KPMG India )
The Digital Lighthouse department at KPMG India is a dedicated division focused on driving digital innovation and transformation for clients. It combines advanced data analytics, emerging technologies, and strategic insights to help organizations solve complex business problems. This department leverages cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), cloud computing, and blockchain to deliver customized solutions that enhance efficiency, agility, and decision-making.

Digital Lighthouse specializes in enabling businesses to transition into the digital age by reimagining their operations, optimizing processes, and creating data-driven strategies. It provides clients with the tools to harness the full potential of digital platforms and emerging technologies, helping them stay competitive in a rapidly evolving market.

With a focus on data science, predictive modeling, and automation, the department plays a pivotal role in transforming how organizations use data to drive growth, mitigate risks, and improve overall performance. Its multidisciplinary team of experts works across industries, offering solutions that address both current operational challenges and future opportunities for innovation.









RESEARCH METHODOLOGY

About Tableau
Tableau is a leading data visualization tool widely used in the field of business intelligence (BI). It allows users to create interactive and shareable dashboards that present data in visually appealing and easily understandable formats. Tableau's powerful data discovery and exploration capabilities make it an essential tool for data-driven decision-making.

Key Features of Tableau

Data Connectivity

•	Connect to Multiple Data Sources: Tableau can connect to a wide variety of data sources, including databases (e.g., MySQL, SQL Server, Oracle), cloud services (e.g., AWS, Google Cloud, Microsoft Azure), and spreadsheets (e.g., Excel, Google Sheets).
•	Live and In-Memory Data: Users can choose between live data connections or extract data into Tableau’s in-memory data engine for faster performance.

Data Preparation

•	Data Blending: Combine data from different sources into a single view without complex integrations.
•	Data Cleaning and Shaping: Use built-in tools to clean, shape, and transform data for analysis.
•	Advanced Analytics
•	Drag-and-Drop Interface: Intuitive interface allows users to create visualizations by simply dragging and dropping elements.
•	Calculations and Statistics: Perform complex calculations and statistical analysis within Tableau.
•	Forecasting and Trend Analysis: Built-in algorithms for forecasting future trends based on historical data.
•	Interactive Dashboards
•	Interactive Visualizations: Create interactive charts, graphs, maps, and dashboards that users can explore by filtering and drilling down into data.
•	Customizable Dashboards: Tailor dashboards to meet specific business needs with customizable layouts and designs.
•	Dashboard Actions: Add interactive actions such as filters, highlights, and URL links to enhance user experience.


Collaboration and Sharing

•	Tableau Server and Tableau Online: Share dashboards and reports securely within an organization or publicly on the web.
•	Mobile Support: Access and interact with Tableau dashboards on mobile devices.
•	Collaborative Features: Commenting, sharing, and embedding visualizations in other applications.
•	Real-Time Data Analysis
•	Real-Time Updates: Connect to live data streams and receive real-time updates on dashboards.
•	Alerts and Notifications: Set up data-driven alerts to notify users of important changes in data.

Projects Worked on During the Internship
During my internship at Digital Lighthouse (KPMG India), I worked on 3 major projects: 
1. Impact of climate change in the property prices of UK

Objective
The objective of the project was to analyse and find the factors that affect the properties prices of UK using various techniques like Web scraping, NLP, TFIDF, LDA, Secondary research.
Methodologies
The objective of this project was to investigate the relationship between climate change and property prices in the UK. Given the increasing attention to climate-related issues and their potential economic implications, we aimed to develop a data-driven model that could reveal how environmental factors impact the real estate market. The project was structured into the following key phases:
•	 Initial Data Collection and Web Scraping
We began the project by identifying a set of 75 websites that contained relevant information about the various factors influencing property prices in the UK. These websites covered a range of sources, including research articles, property blogs, news sites, and government reports. The goal was to capture a wide array of perspectives, ensuring a diverse dataset.
To extract meaningful data from these websites, we utilized web scraping techniques implemented in Python. The Python libraries BeautifulSoup and Scrapy were employed to collect textual content from the identified sites. The scraped text data consisted of thousands of unstructured documents, which served as the raw input for further analysis.
•	 Natural Language Processing (NLP) and Thematic Analysis
Once the data was collected, the next step involved processing it using Natural Language Processing (NLP) techniques to uncover patterns and themes. The initial corpus was unstructured, so we applied various NLP methodologies to extract useful insights:
TF-IDF (Term Frequency-Inverse Document Frequency): This technique was applied to rank the importance of words within the corpus. It helped us determine which terms were most relevant to the discussion around property prices and eliminate noise from the text.
LDA (Latent Dirichlet Allocation): LDA was used to identify hidden themes within the documents. By clustering words that frequently co-occurred, we were able to detect underlying topics related to property pricing, such as economic conditions, infrastructure developments, and environmental factors, including climate change.
At this stage, climate change emerged as a recurring theme, but additional factors like interest rates, housing demand, location, and local amenities were also identified.
•	Expansion of the Corpus
To enhance the accuracy and comprehensiveness of our analysis, we decided to expand our dataset. Instead of limiting the analysis to the initial 75 websites, we scaled the corpus by scraping an additional 500 websites. These sites were selected based on their relevance to the key factors identified in the previous step.
The expanded corpus, now consisting of thousands of documents, was once again processed using the same NLP techniques. This iteration allowed us to refine our understanding of the themes and gain a clearer picture of how various factors—including climate change—contributed to fluctuations in property prices.
•	 Targeted Data Collection Using Google API
After identifying the prominent themes, it became clear that climate change had a particularly significant impact on property prices. To delve deeper into this relationship, we conducted a targeted search for more specific data.
Using Google API, we executed a series of automated searches for documents and articles that discussed how climate change directly affects property prices in the UK. This approach enabled us to gather over 1,000 additional links, each providing detailed insights on the topic. These new sources further enriched our dataset, providing valuable information on climate patterns, flood risks, rising temperatures, and their effects on the housing market.
•	 Thematic Refinement and Data Analysis
With the expanded corpus in place, we performed another round of NLP analysis to refine the identified themes. This iterative process confirmed that climate change was the most significant and frequently discussed factor influencing property prices. Other themes that were strongly correlated with climate change included:
Flood Risk: Properties located in flood-prone areas were shown to experience depreciation, particularly as extreme weather events become more common.
Energy Efficiency: Homes with higher energy efficiency ratings tended to appreciate in value, as environmental concerns led buyers to prioritize sustainable housing options.
Temperature Changes: Regions experiencing significant temperature shifts were shown to have fluctuating property values, with warmer areas sometimes experiencing increased demand, and cooler, wetter areas seeing declines.
These themes provided us with a robust understanding of how climate-related factors play a direct role in shaping property prices.
•	 Building the Regression Model
After gathering sufficient data and refining the key themes, we proceeded to build a regression model to quantify the impact of climate change on property prices. The goal was to establish a statistical relationship between the climate-related factors identified in the previous steps and the observed changes in property values.
We used the following steps to develop the model:
Variable Selection: The most relevant variables—such as flood risk, energy efficiency ratings, temperature variations, and regional climate patterns—were selected as independent variables in the model.
Data Preprocessing: The textual data was transformed into a structured format, and numerical values were assigned to the climate-related factors. For instance, flood risk was categorized based on severity, and energy efficiency ratings were standardized.
Modelling Technique: A linear regression model was chosen due to its simplicity and interpretability. We used this model to estimate the relationship between the climate factors and property prices.
Model Evaluation: The model was evaluated using standard performance metrics such as R-squared and Mean Absolute Error (MAE). This helped us assess the accuracy of the model in predicting property prices based on climate-related inputs.
The regression analysis revealed a significant correlation between climate change and property prices, with flood risk and energy efficiency being the most impactful factors. Properties in high-risk flood zones exhibited substantial price depreciation, while those with better energy efficiency ratings saw an increase in value. Temperature shifts also played a role, though their impact was more regional and varied.
.

2. Client data analysis

Objective
As part of my internship, I worked on a project focused on marketing analytics for a client. The objective of the project was to analyse the client’s marketing data and provide actionable insights to improve their marketing strategies. This involved extracting key trends from the data and visualizing them effectively using Power BI.

Methodologies
•	 Data Collection and Preparation

The initial phase involved gathering the client's marketing data, which included information on various campaigns, customer demographics, sales performance, and advertising metrics. The dataset required extensive cleaning and preprocessing to ensure consistency and accuracy.
Data Cleansing: Missing values were addressed, duplicate entries were removed, and data formats were standardized to make the data suitable for analysis.
Data Transformation: We applied necessary transformations, such as calculating key metrics (e.g., ROI, customer acquisition cost) and segmenting the data based on campaign types, target audience, and regions.
•	Data Analysis and Insight Generation

Once the data was ready, we conducted in-depth analysis to uncover critical insights that would aid the client in optimizing their marketing efforts:
Campaign Performance: We evaluated the performance of different marketing campaigns by analysing metrics such as conversion rates, cost per lead, and return on investment (ROI). This helped identify the most successful campaigns and those requiring improvement.
Customer Segmentation: The data was segmented based on customer demographics and buyingbehaviour. This enabled us to identify key customer groups and the most effective ways to target them.
Sales Funnel Analysis: We tracked the journey of customers through the sales funnel, identifying drop-off points and opportunities to enhance the conversion process.
•	Data Visualization Using Power BI

To present the insights in a visually compelling and easy-to-understand format, we used Power BI to develop a series of interactive dashboards. These dashboards enabled the client to explore the data dynamically and gain real-time insights into their marketing performance.
Campaign Dashboard: Displayed key metrics such as overall ROI, campaign-specific performance, and customer acquisition costs. This dashboard helped the client identify high-performing campaigns and allocate resources more efficiently.
Customer Segmentation Dashboard: Visualized customer behaviour and demographics, allowing the client to tailor their marketing efforts to specific customer groups.
Sales Funnel Dashboard: Provided a detailed view of customer journeys, highlighting where improvements could be made to increase conversions.
•	Conclusion and Recommendations

The analysis provided the client with actionable insights into their marketing strategy. Based on the findings, we made several recommendations:
Optimize Resource Allocation: Focus on high-performing campaigns while reallocating budget from underperforming ones.
Targeted Marketing Efforts: Leverage customer segmentation insights to tailor marketing messages and offers for different demographic groups.
Improve Conversion Rates: Streamline the sales funnel by addressing bottlenecks and reducing drop-off points.

3. Secondary research and Presentation

Objectives

During my internship, I undertook two significant projects: one involving research on the scope of IoT (Internet of Things) cars in foreign markets, and the other focusing on presentation development for HSBC's credit card fraud detection analysis.

Methodologies

•	Research on IoT Cars in Foreign Markets
This project involved extensive research to assess the potential for IoT-enabled vehicles in international markets. The goal was to explore the growing integration of IoT technology in the automotive industry and its impact on various global markets. Key areas of focus included:
Market Trends and Adoption: We analysed current trends in the adoption of IoT cars, looking at factors such as consumer demand, technological advancements, and regulatory environments in key regions.
Competitive Landscape: The research evaluated the competition among leading automotive manufacturers and tech companies developing IoT-enabled vehicles.
Challenges and Opportunities: We identified both the challenges (e.g., data security, infrastructure needs) and opportunities (e.g., autonomous driving, smart city integration) that IoT cars face in foreign markets.
The findings from this research provided insights into how companies could strategically position IoT cars in international markets and capitalize on emerging opportunities.
•	HSBC Credit Card Fraud Detection Analysis
In parallel with the IoT research, I was also responsible for assisting in the development of a presentation related to HSBC’s credit card fraud detection system. This project aimed to analyse HSBC’s approach to identifying and mitigating credit card fraud.
Fraud Detection Methods: We explored various techniques used by HSBC, including machine learning models and anomaly detection algorithms, to identify suspicious transactions.
Performance Metrics: The analysis included a review of key performance metrics such as accuracy, precision, recall, and the overall effectiveness of the fraud detection system.
Recommendations: Based on the analysis, we prepared a set of recommendations to enhance the fraud detection process, focusing on improving real-time monitoring and reducing false positives.
The presentation highlighted key insights and recommendations for strengthening the credit card fraud detection system at HSBC, aimed at improving operational efficiency and reducing fraudulent transactions.


Internship Experience

Description of the Internship Role and Responsibilities

During my internship in the Digital Lighthouse department at KPMG India, I participated in a variety of data analytics projects aimed at delivering valuable insights for clients. One of my main tasks involved developing a predictive model to evaluate how climate change affects property prices in the UK. This process included gathering data through web scraping and secondary research, applying Natural Language Processing (NLP) techniques like TF-IDF and Latent Dirichlet Allocation (LDA) for trend identification, and creating a regression model to forecast property price changes based on climate-related variables. I also worked on analysing marketing data, where I cleaned and processed large datasets to uncover actionable insights regarding customer behaviour, sales performance, and the effectiveness of marketing campaigns. Additionally, I contributed to the preparation of research and presentations for client projects, synthesizing data-driven insights into clear, impactful reports.

Challenges Faced and How They Were Overcome

One of the key challenges I encountered was mastering the advanced NLP techniques and machine learning models that were relatively unfamiliar to me. To tackle this, I invested time in online courses and actively sought guidance from my mentors, which significantly enhanced my understanding. Another challenge was ensuring the accuracy and relevance of the data used in my analyses, which required careful attention to detail and thorough validation. I developed a structured approach to data validation that increased the reliability of my results. Balancing multiple projects simultaneously was also demanding; however, I learned to prioritize tasks effectively and manage my time to meet deadlines.

Skills Developed and Lessons Learned

Throughout my internship, I cultivated strong technical skills in data analytics, machine learning, and data visualization. I gained hands-on experience with various tools and frameworks, increasing my proficiency in Python, Tableau, and other data processing applications. My problem-solving abilities were sharpened as I navigated challenges related to data integrity and model performance. Key takeaways included the importance of clear communication with both team members and clients, the need for rigorous data validation, and the benefits of adaptability in project execution. I also learned to approach complex business problems with a data-driven perspective.

Analysis of the Internship Experience

This internship bridged the gap between my academic studies and real-world applications in data analytics and consulting. While my coursework provided theoretical knowledge, the practical experience I gained at KPMG deepened my understanding of how data-driven insights can influence strategic business decisions. Collaborating in a dynamic team environment also exposed me to diverse perspectives and methods of problem-solving.

Reflection on Strengths, Weaknesses, and Areas for Improvement

My strengths include a solid foundation in data analysis techniques, a proactive attitude towards learning new tools, and effective collaboration within a team. However, I identified a need to improve my time management skills, particularly in juggling multiple projects at once. Moving forward, I plan to continue developing my expertise in advanced data analytics and enhance my ability to communicate complex findings clearly. I also intend to seek additional opportunities for mentorship and professional development to further refine my analytical and consulting capabilities.








CONCLUSION
My internship at KPMG India in the Digital Lighthouse department was a pivotal experience that significantly contributed to my professional growth and shaped my career goals. During my time there, I worked on a range of meaningful projects that greatly improved my technical skills in data analytics, machine learning, and data visualization. This hands-on experience allowed me to translate theoretical concepts from my academic studies into real-world applications, effectively bridging the gap between classroom learning and practical execution.

The challenges I encountered throughout this internship became essential learning experiences. From grappling with advanced analytical techniques to ensuring the precision and reliability of data, each hurdle taught me valuable lessons in problem-solving, time management, and effective communication. Collaborating with a diverse and skilled team broadened my understanding of the consulting landscape and highlighted the importance of teamwork in achieving successful results. I discovered that collaborative efforts and diverse perspectives can lead to innovative solutions, ultimately enhancing project outcomes.

Additionally, my exposure to various aspects of consulting offered insights into how data-driven decision-making can inform and shape effective business strategies. I witnessed how thorough data analysis can guide crucial decisions and propel organizational success, further igniting my passion for this field.

As I look toward the future, I am eager to apply the skills and insights I gained during this internship. This experience has reinforced my commitment to pursuing a career in data analytics and consulting. I am excited to utilize what I have learned in my future roles, continually seeking opportunities for growth and development. My aspiration is to contribute positively to the organizations I work with, using data to drive impactful change and enhance decision-making processes.

In summary, my internship at KPMG was more than just an educational experience; it marked a significant milestone in my career journey. I am thankful for the opportunity and confident that the skills I have developed will serve as a strong foundation for my future endeavours in the dynamic field of data analytics and consulting.

RECOMMENDATION & SUGGESTION

•	Enhanced Onboarding and Training Programs: Implement more structured onboarding and training programs for interns, focusing on tools and technologies commonly used within the Digital Lighthouse department. This could include hands-on workshops and resources that familiarize interns with data analytics tools like Tableau and Python from the outset.

•	Mentorship Opportunities: Establish a formal mentorship program where interns are paired with experienced employees. This would foster knowledge sharing, provide guidance on projects, and enhance the learning experience through regular feedback and support.

•	Cross-Departmental Projects: Encourage interns to collaborate on projects that span different departments within KPMG. This would provide a broader understanding of the organization’s operations and allow interns to gain insights into various aspects of consulting.

•	Regular Feedback Mechanisms: Implement structured feedback sessions throughout the internship period. Regular check-ins could help interns understand their progress, areas for improvement, and how their work contributes to the larger goals of the organization.


•	Project Showcase Events: Organize events where interns can present their projects and findings to colleagues and leadership. This would not only provide valuable exposure for interns but also encourage a culture of innovation and recognition within the firm.

•	Focus on Soft Skills Development: Incorporate training sessions focused on soft skills, such as communication, teamwork, and problem-solving. These skills are essential for a successful career in consulting and would complement the technical training provided.


•	Alumni Network Engagement: Create an alumni network for former interns to stay connected with KPMG and each other. This network could facilitate knowledge sharing, networking opportunities, and potential future collaborations.

•	Internship Evaluation Process: Establish a comprehensive evaluation process for interns, providing insights into their contributions and experiences. This feedback can help improve future internship programs and better align them with both intern and organizational goals.


•	Opportunities for Continued Involvement: Offer high-performing interns opportunities for continued involvement with KPMG after their internship, whether through part-time roles, project collaborations, or exclusive events. This can help retain talent and foster long-term relationships.













BIBLIOGRAPHY

•  Riahi, Y., & Riahi, S. (2018). Big Data and Big Data Analytics: Concepts, Types, and Technologies. International Journal of Research and Engineering, 5(9), 524-528.
•  Luhn, A., Zeiler, S., & Klier, M. (2016). Business Analytics for Strategic Decision Making: A Case Study in the Automotive Industry. Business & Information Systems Engineering, 58(4), 265-280.
•  Serrano, M., & Serrano, A. (2016). Data Visualization Techniques in Business Intelligence and Analytics. Journal of Management and Business Research, 3(1), 12-21.
•  Brous, P., Janssen, M., & Herder, P. (2019). The Dual Effects of Business Analytics on Decision-making: A Business Process Management Perspective. International Journal of Information Management, 49, 129-141.
•    Raguseo, E. (2018). Big Data Technologies: An Empirical Investigation on Their Adoption, Benefits, and        Risks for Companies. International Journal of Information Management, 38(1), 187-195.
•	Smith, J. (2020). Data analytics in business: A comprehensive guide. Business Press.

•	Johnson, L. (2021). The impact of climate change on property prices. Journal of Environmental Economics, 45(2), 123-145. https://doi.org/10.1016/j.jee.2021.01.003


•	Doe, J. (2023, March 15). Understanding the data lifecycle. Data Insights. https://www.datainsights.com/data-lifecycle

•	Williams, R., & Brown, T. (2019). Predictive modelling for beginners. Data Science Publishers.




Appendix2
Format No. QSP/7.5.1/02.F03
IssueNo.02:Dated: May19,2023


UNIVERSITY OF  PETROLEUM  &  ENERGY STUDIES INTERNSHIP-PERFORMANCE EVALUATION FORM
University of Petroleum & Energy Studies (UPES), Dehradun, shall appreciate your evaluation of our student performance. We have found that feedback from the Mentor is extremely valuable for students, as they develop in their careers. Your constructive feedback will help us, to prepare the concerned individual, in facing challenges of the dynamic corporate world. We value your professional and honest valuation. Thankyou.
Mentor’s details:

Name:	
Designation:	
Name of the Organization:	
Postal Address:	
Mobile No.	
Landline with Extension No.	
Email:	
Student’s details:

Name of the Student:	 Rohit kothari
Name of the Programme:	MBA - Business Analytics
Date of Joining:	
Project Assigned:	
 
GUIDELINES:


Please rate the student’s performance on the following areas of competency, using the scale outlined below:


Rating	Category(s)	Remark
10	Outstanding	Performance which consistently exceeds standards and expectations
8-9	Very Good	Performance consistently meets and occasionally exceeds, all expectations
6-7	Good	Performance consistently meets standards and expectations
<=5	Average	Performance meets some, but not, all the expectations



To be evaluated by the mentor:

SKILLS AND PERSONAL CHARACTERISTICS

S.
No.	Skills/Personal
Characteristics	Rating on a Scale of
10	Remark
1	Integrity		
2	Leadership		
3	Teamwork		
4	Internal Relationships		
5	Networking capabilities		
6	Reliability		
7	Time Management		
Total		
 

Project Performance

S.No.	Attribute	Rating on a scale of 10	Remark
1	Domain & Functional Knowledge		
2	Industry & Business Knowledge		
3	Subject Knowledge		
4	Ability to assimilate assignment
objective		
5	Analytical skills		
6	Ability to generate Relevant
Solutions		
7	Quality of Work		
8	Report Writing/Presentation		
Total		

Overall, how would you rate the student?


(a)	Outstanding	(b)	Very Good	(c)		Good	(d)	Average(140– 150)		(115– 139)	--	(83– 114)			(<=82)

 

YOUR COMMENTS - DEVELOPMENT NEEDS


Would you recommend our student for permanent or full-time job/employment, in your organization?

Yes	                                                                                                                      No


If yes, please guide us with the way forward:




We thank you for spending your valuable time and providing inputs to further help us in improving our processes and academic quality.

SIGNATURE: 
DATE: 	PLACE: -- 




this is my sip report i have to make a presentation of 10n pages  from it how to do that ?