Hello there! I'm Suraj Gusain, a passionate data enthusiast and recent B.Tech graduate (Class of 2021) with a strong foundation in computer science and data analysis. I am thrilled to share my journey, skills, and projects with you.

• Bachelor of Technology (B.Tech) in Computer Science and Engineering, Kurushetra University, 2021
• CGPA: 8.4/10.0
• I attempted the GATE exam in 2022 in the field of Computer Science and Engineering to enhance my knowledge and skills. Although I did not qualify, this experience has motivated me to further strengthen my
  expertise in this area. Through continuous learning and hands-on projects, I am committed to advancing my knowledge and contributing meaningfully to the world of data analysis and technology.

• Data Analyst Certification, 6-Month Course, Skilledge, May 2022-Nov 2022
• HackerRank Basic Assessment Test, Completed with Distinction, May 2022
• HackerRank Intermediate Assessment Test, Completed with Distinction, Auguest 2022
• Meriskill Virtual Internship Completion, July 2023 -Sep2023

• Programming Languages: Python, SQL,
• Data Analysis Tools: Pandas, NumPy, Matplotlib, Seaborn
• Databases: MySQL, SQLite
• Tools: Jupyter Notebook, Git, GitHub,Mysql Workbench,Google collab
• Others: Data Cleaning, Data Visualization, Statistical Analysis
• Data Visualization Tools: Excel,Power Bi,Power Query,Dax,Data Modelling
• I’m currently learning Machine Learning

𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁: 𝗔𝘁𝗹𝗶𝗤 𝗠𝗮𝗿𝘁 𝗰𝘂𝗿𝗿𝗲𝗻𝘁𝗹𝘆 𝗳𝗮𝗰𝗲𝘀 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗵𝗲𝗿𝗲 𝗮 𝗳𝗲𝘄 𝗸𝗲𝘆 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗱𝗶𝗱 𝗻𝗼𝘁 𝗿𝗲𝗰𝗲𝗶𝘃𝗲 𝘁𝗵𝗲𝗶𝗿 𝗮𝗻𝗻𝘂𝗮𝗹 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁𝘀 𝗱𝘂𝗲 𝘁𝗼 𝗶𝘀𝘀𝘂𝗲𝘀 𝗶𝗻 𝗳𝘂𝗹𝗹 𝗼𝘃𝗲𝗿 𝗮 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁𝗲𝗱 𝗽𝗲𝗿𝗶𝗼𝗱, 𝘄𝗵𝗶𝗰𝗵 𝗰𝗼𝘂𝗹𝗱 𝗵𝗮𝗿𝗺 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗼𝘃𝗲𝗿𝗮𝗹𝗹 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘄𝗮𝗻𝘁𝘀 𝘁𝗼 𝗮𝗱𝗱𝗿𝗲𝘀𝘀 𝘁𝗵𝗶𝘀 𝗶𝘀𝘀𝘂𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗲𝘅𝗽𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗼 𝗼𝘁𝗵𝗲𝗿 𝗰𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗶𝘀 𝘀𝗲𝗲𝗸𝗶𝗻𝗴 𝗮 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘁𝗼 𝘁𝗿𝗮𝗰𝗸 𝘁𝗵𝗲 '𝗢𝗻 𝘁𝗶𝗺𝗲' 𝗮𝗻𝗱 '𝗜𝗻 𝗙𝘂𝗹𝗹' 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗹𝗲𝘃𝗲𝗹𝘀 𝗳𝗼𝗿 𝗮𝗹𝗹 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗱𝗮𝗶𝗹𝘆 𝘁𝗼 𝗲𝗻𝘀𝘂𝗿𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗹𝗲𝘃𝗲𝗹𝘀 𝗮𝗿𝗲 𝗺𝗮𝗶𝗻𝘁𝗮𝗶𝗻𝗲𝗱 𝗼𝗿 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱."

📌 𝗜𝗻 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗞𝗲𝘆 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 (𝗞𝗣𝗜𝘀), "𝗢𝗧%" 𝗰𝗮𝗻 𝘀𝘁𝗮𝗻𝗱 𝗳𝗼𝗿 "𝗢𝗻-𝗧𝗶𝗺𝗲 𝗣𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲." 𝗜𝘁 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝘁𝗵𝗲 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗼𝗿𝗱𝗲𝗿𝘀 𝗼𝗿 𝘀𝗵𝗶𝗽𝗺𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗮𝗿𝗲 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗼𝗿 𝗳𝘂𝗹𝗳𝗶𝗹𝗹𝗲𝗱 𝗼𝗻 𝘁𝗶𝗺𝗲 𝗮𝘀 𝗽𝗹𝗮𝗻𝗻𝗲𝗱, 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗼𝗳 𝗮 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗲𝘀.

📌 𝗜𝗙 %:𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗮𝘁 𝘁𝗵𝗲 𝗼𝗿𝗱𝗲𝗿 𝗹𝗲𝘃𝗲𝗹. 𝗜𝘁 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀 𝗶𝗳 𝗮𝗻 𝗼𝗿𝗱𝗲𝗿 𝗶𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗶𝗻 𝗳𝘂𝗹𝗹 𝗮𝘀 𝗽𝗲𝗿 𝘁𝗵𝗲 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝗲𝗱 𝗾𝘂𝗮𝗻𝘁𝗶𝘁𝘆 𝗯𝘆 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿.

📌 𝗢𝗧𝗜𝗙%:𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝗱 𝗮𝘁 𝘁𝗵𝗲 𝗼𝗿𝗱𝗲𝗿 𝗹𝗲𝘃𝗲𝗹. 𝗜𝘁 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗲𝘀 𝗶𝗳 𝗮𝗻 𝗼𝗿𝗱𝗲𝗿 𝗶𝘀 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗲𝗱 𝗕𝗢𝗧𝗛 𝗶𝗻 𝗳𝘂𝗹𝗹 𝗮𝗻𝗱 𝗢𝗻 𝗧𝗶𝗺𝗲 𝗮𝘀 𝗽𝗲𝗿 𝘁𝗵𝗲 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗼𝗿𝗱𝗲𝗿 𝗿𝗲𝗾𝘂𝗲𝘀𝘁.

𝗧𝗵𝗶𝗻𝗴𝘀 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁:-

1. Did some study on 𝗸𝗲𝘆 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 Supply Chain 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 and created 𝗗𝗔𝗫 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 for the same.
2. Effective usage of DAX.
3. Creating calculated columns.
4. Data Modelling

• Technologies used: Power BI,Excel,Dax,Data Modelling

  Linkedin Engagement

📌 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗞𝗣𝗜'𝘀 𝗜𝗻 𝗛𝗼𝘀𝗽𝗶𝘁𝗮𝗹𝗶𝘁𝘆 𝗱𝗼𝗺𝗮𝗶𝗻

💡 𝗥𝗲𝘃𝗣𝗔𝗥 (𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗣𝗲𝗿 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗥𝗼𝗼𝗺): 𝗜𝘁 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝘀 𝘁𝗵𝗲 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗲𝗮𝗿𝗻𝗲𝗱 𝗽𝗲𝗿 𝗿𝗼𝗼𝗺, 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗶𝘁'𝘀 𝘀𝗼𝗹𝗱 𝗼𝗿 𝗻𝗼𝘁, 📌 𝗥𝗲𝘃𝗣𝗔𝗥: 𝗧𝗼𝘁𝗮𝗹 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗲𝗮𝗿𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝗿𝗼𝗼𝗺 𝘀𝗮𝗹𝗲𝘀/𝘁𝗼𝘁𝗮𝗹 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗿𝗼𝗼𝗺𝘀.

💡 𝗔𝗗𝗥 (𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗗𝗮𝗶𝗹𝘆 𝗥𝗮𝘁𝗲): 𝗔𝗗𝗥 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝘁𝗵𝗲 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗽𝗿𝗶𝗰𝗲 𝗼𝗿 𝗿𝗮𝘁𝗲 𝘁𝗵𝗮𝘁 𝗮 𝗵𝗼𝘁𝗲𝗹 𝗰𝗵𝗮𝗿𝗴𝗲𝘀 𝗳𝗼𝗿 𝗶𝘁𝘀 𝗿𝗼𝗼𝗺𝘀 𝗽𝗲𝗿 𝗱𝗮𝘆. 𝗜𝘁'𝘀 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗯𝘆 𝗱𝗶𝘃𝗶𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝘁𝗼𝘁𝗮𝗹 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗲𝗮𝗿𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝗿𝗼𝗼𝗺 𝘀𝗮𝗹𝗲𝘀 𝗯𝘆 𝘁𝗵𝗲 𝘁𝗼𝘁𝗮𝗹 𝗻𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗿𝗼𝗼𝗺𝘀 𝘀𝗼𝗹𝗱. 📌 𝗔𝗗𝗥 = 𝗧𝗼𝘁𝗮𝗹 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 / 𝗡𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗥𝗼𝗼𝗺𝘀 𝗦𝗼𝗹𝗱

💡 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗥𝗲𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗣𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 = (𝗔𝗰𝘁𝘂𝗮𝗹 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 / 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗥𝗲𝘃𝗲𝗻𝘂𝗲) * 𝟭𝟬𝟬

💡 𝗡𝗼-𝗦𝗵𝗼𝘄 𝗥𝗮𝘁𝗲: 𝗧𝗵𝗲 𝗻𝗼-𝘀𝗵𝗼𝘄 𝗿𝗮𝘁𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗲𝗿𝗰𝗲𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗿𝗲𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝗿𝗲𝘀𝘂𝗹𝘁 𝗶𝗻 𝗴𝘂𝗲𝘀𝘁𝘀 𝗻𝗼𝘁 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘂𝗽. 📌 𝗡𝗼-𝗦𝗵𝗼𝘄 𝗥𝗮𝘁𝗲 = (𝗡𝘂𝗺𝗯𝗲𝗿 𝗼𝗳 𝗡𝗼-𝗦𝗵𝗼𝘄𝘀 / 𝗧𝗼𝘁𝗮𝗹 𝗕𝗼𝗼𝗸𝗶𝗻𝗴𝘀) * 𝟭𝟬𝟬

💡 𝗗𝗦𝗥𝗡 :𝗧𝗵𝗶𝘀 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗲𝗹𝗹𝘀 𝗼𝗻 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝗿𝗼𝗼𝗺𝘀 𝗮𝗿𝗲 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝘀𝗲𝗹𝗹 𝗳𝗼𝗿 𝗮 𝗱𝗮𝘆 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗶𝗻𝗴 𝗮 𝘁𝗶𝗺𝗲 𝗽𝗲𝗿𝗶𝗼𝗱. 📌 𝗗𝗦𝗥𝗡=𝗧𝗼𝘁𝗮𝗹 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆/𝗡𝗼 𝗼𝗳 𝗱𝗮𝘆𝘀.

𝗧𝗵𝗶𝗻𝗴𝘀 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱 𝗳𝗿𝗼𝗺 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁:-

1. Did some study on 𝗸𝗲𝘆 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗛𝗼𝘀𝗽𝗶𝘁𝗮𝗹𝗶𝘁𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 and created𝗗𝗔𝗫 𝗺𝗲𝗮𝘀𝘂𝗿𝗲𝘀 for the same.
2. Effective usage of DAX.
3. Creating calculated columns.
4. Data Modelling.

• Technologies used: Power BI,Excel,Dax,Data Modelling

𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻:

1. Total Revenue is overall $𝟭.𝟲𝟵𝗕
2. ADR is 𝗥𝘀 𝟭𝟮,𝟲𝟵𝟲 3.RevPAR is 𝗥𝘀 𝟳,𝟯𝟯𝟳
3. The occupancy rate is 𝟱𝟳.𝟳𝟵%
4. Realization rate is 𝟴𝟬.𝟭𝟴%
5. Cancellation rate is 𝟮𝟰.𝟴𝟰%
6. Mumbai has the highest RevPAR Booking 𝟭𝟱.𝟯𝟵𝗞
7. Hyderabad has the lowest RevPAR 𝟵.𝟯𝟮𝗸 Booking
8. Elite has the highest Revenue contribution per Room Class 𝟯𝟮.𝟳𝟵%.
9. Standard has the lowest Revenue contribution per Room Class 𝟭𝟴.𝟭𝟮%

Linkedin Engagement

About Challenge: Domain: F & B Function: Marketing
CodeX is a German beverage company aiming to make its mark in the Indian market. A few months ago, they launched their energy drink in 10 cities in India. Their Marketing team is responsible for increasing brand awareness, market share, and product development. They conducted a survey in those 10 cities and received results from 10k respondents.

Task Completed

1. Demographic Insights Key Insights: 1.1 Among 10 thousand respondents, 6038 are male, indicating a 60% preference for energy drinks. 1.2 Survey results reveal energy drinks' popularity among youngsters, with over 50% aged 19-30; considering ages 15-30 raises the percentage to 70%

2. Consumer Preferences: Key Insights: 2.1 Caffeine, known for enhancing attention and alertness, is the primary anticipated ingredient in energy drinks, closely followed by vitamins 2.2 Compact & Portable Cans are in high demand, trailed by Innovative Bottle Designs.

3. Competition Analysis: Key Insights: 3.1 The top reason for choosing the brands by consumers is brand reputation. 3.2 "Sky9" is notably effective, "Gangster" is remarkably healthy, while "Bepsi" raises concerns for being more dangerous.

4. Marketing Channels and Brand Awareness: Key Insights: 4.1 Online ads prove highly effective for reaching a wide audience swiftly and economically, as observed earlier

5. Brand Penetration: Key Insights 5.1 Among 980 individuals, 455 are familiar with our brand, aligning their average taste rating of 3.3 with the industry average

6. Purchase Behavior: Key Insights 6.1 Supermarkets are the predominant choice for consumers purchasing energy drinks. 6.2 Sports, exercise, and studying/working late; this data underscores greater youth consumption. 6.3 40% of consumers anticipate no packaging change, while 39% are receptive to experimenting with Limited Edition Packaging.

7. Product Development Key Insights 7.1 We should increase Effectiveness 7.2 We should improve and Reduce Sugar Conten

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