VENKATESHH SUGANDHAM

Aspiring Data Scientist with a B.Tech in Electrical Engineering from IIT Jammu, specializing in Python, SQL, and C++. Experienced in data science, machine learning, and data visualization, with a proven ability to develop and deploy predictive models. Demonstrated academic excellence and practical experience through various internships and projects. Adept at solving complex problems and committed to continuous professional growth.

• Certificate Program on Data Science [360 Digitmg] : Completed the 360DigiTMG Certified Data Science Program accredited by NASSCOM and approved by the Government of India.
• Fractal data Science Professional Certificate [Coursera] : Acquired expertise in structured problem-solving, SQL proficiency, data visualization with Power BI, Python programming for data manipulation and machine learning, data storytelling, and critical data analysis for informed decision making.
• Relevant Courses [Bachelors] : Machine Intelligence and Learning | Cognitive Robotics | Probability and Stochastic Processes | Introduction to Computer Science | Data Structures and Algorithms : Gained expertise in machine learning algorithms, cognitive robotics principles, probability theories, programming fundamentals, and algorithm design.

Tender Price Prediction Model :

Business problem : In the competitive landscape of tendering, securing contracts relies significantly on offering competitive pricing. However, accurately determining the optimal bid amount can be challenging, especially without insights into competitors’ pricing strategies. This uncertainty often leads to two critical issues, losing tenders due to overpricing or risking profit margins by underpricing.

Description:

• Developed a machine learning model to predict tender prices for construction projects, aiding clients in making informed pricing decisions.
• Utilized Python for data preprocessing, feature engineering, and model training.
• Implemented multiple regression algorithms, including Linear Regression, Decision Tree and Gradient Boosting, to determine the most accurate predictive model.
• Evaluated model performance using metrics such as R-squared, Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE).
• Enhanced model accuracy by applying techniques such as hyperparameter tuning and cross-validation.

Key Contributions:

• Cleaned and processed large datasets, ensuring data quality and consistency.
• Visualized data insights using Power BI, creating dashboards for stakeholders.
• Deployed the final model using Streamlit, creating an interactive user interface for clients to input data and obtain price predictions.
• Collaborated with a team of data scientists and project managers to align the model with business objectives.

Technologies Used:

• Programming Languages: Python
• Libraries: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Joblib
• Tools: Power BI, Streamlit

• Published paper on increasing internal quantum efficiency of UV-C LED in the 21st conference of NUSOD.
• Member of the winning team in the nationwide competition, Smart India Hackathon 2019.
• Ranked among the top 1 percentile in the JEE Advanced examination.

• PoR, Kritash (Social Welfare Club, IIT JMU) : Volunteered in the first-ever Blood Donation Camp and Plantation Drive at IIT JMU.
• PoR, Chitraka (Media and Film Making Club, IIT JMU) : Served as media member for Industry Conclave'19, Infinito'18, Convoquer'18 and Technunctus'19. Served as a media member for Institute Calender and JAGTI, the Quarterly Newsletter of IIT JMU. Served as media manager for the 2018 Foundation Program of IIT JMU.
• PoR, NSS, IIT JMU : Volunteered in creating a stop-motion COVID-19 awareness video during the lockdown.