Aug 2022 – May 2025 · 2 yrs 9 mos · New Delhi, Delhi, India

• At Kreate Energy Pvt Ltd, I engage in a multifaceted role as a data scientist, orchestrating pivotal tasks that drive innovation and precision in the renewable energy sector. My daily responsibilities encompass a spectrum of activities, commencing with comprehensive data gathering and rigorous analysis to extract meaningful insights. Collaborating closely with domain experts, I leverage their specialized knowledge to inform and enrich the data-driven strategies.
• An integral part of my role involves crafting robust machine learning models tailored to the specific needs of our projects. By amalgamating domain insights and meticulously curated analyses, I develop sophisticated statistical and machine learning models. These models play a pivotal role in predicting electricity prices and demand, facilitating informed decision-making in the realms of solar power, wind energy, and weather prediction.
• One of my significant achievements includes spearheading the creation of approximately 10 highly accurate models for JBVNL. These models were meticulously designed to forecast demand values across various time frames, consistently delivering an impressive average accuracy rate of 93%. A testament to my commitment to excellence, I continuously fine-tuned these models, employing a range of techniques including linear regression, random forest, decision trees, and xgboost algorithms.
• Through my role at Kreate Energy, I am dedicated to harnessing data science methodologies to not just meet but exceed business requirements. My focus remains steadfast on enhancing accuracy, optimizing models, and driving impactful outcomes in the renewable energy landscape.

Aug 2022 – Aug 2022 · 0 mo · New Delhi, India

• During my tenure at Medtour Easy, I undertook a dynamic internship focused on delving into fitness data analysis. Immersed in a vibrant and innovative environment, I meticulously dissected fitness-related datasets, conducting comprehensive analyses to derive meaningful insights.
• My responsibilities revolved around navigating diverse fitness data sets, employing analytical tools and techniques to extract valuable patterns and trends. This experience honed my skills in data analysis, allowing me to uncover actionable insights that contributed to the understanding of health and wellness metrics.
• This internship provided me with a platform to apply data-driven methodologies within the realm of fitness, fostering a deeper understanding of the significance of data analysis in optimizing health-related strategies. It further enriched my acumen in handling real-world data, strengthening my foundation in data analysis and its practical applications within the healthcare and wellness domain.

Jun 2022 – Aug 2022 · 2 mos · Assam, India

• At FEYN Labs, I embarked on an engaging internship centered around Electronic Vehicle (EV) market segmentation and car price prediction. Within this dynamic environment, I delved into the realm of automotive data analysis with a specific focus on EVs.
• During this internship, I actively contributed to a project aimed at segmenting the electronic vehicle market. My responsibilities involved meticulously analyzing market data to identify distinct segments within the EV industry. Additionally, I spearheaded a car price prediction initiative, leveraging data-driven methodologies to forecast prices within this rapidly evolving market.
• This experience equipped me with invaluable insights into the complexities of the automotive industry, particularly in the context of EVs. It honed my abilities in market analysis, predictive modeling, and data-driven decision-making specific to the realm of electric vehicles.
• Working at FEYN Labs provided an immersive platform to apply data science techniques within the automotive sector, solidifying my understanding of market segmentation and predictive modeling in this innovative and transformative industry.

Jan 2022 – Mar 2022 · 2 mos · Banglore

• 1)To perform customer segmentation on the basis of the buying habits and to know the
• customer characteristics and patterns so that we can target customer precisely.
• 2)Applied Feature Engineering to obtain new features such as RFM, RFMGroup , and
• RFMScore for getting more details about the customers purchasing behavior.
• 3)Evaluated the optimal clusters using Silhouette Score and Elbow method and
• visualized the inter-cluster separation and intra-cluster similarities.
• 4)Developed K-means clustering model, resulting in 3 clusters with a Silhouette score 0.39, which helps the Business to devide the Customers in a more accurate manner.

Oct 2021 – Dec 2021 · 2 mos · Banglore

• 1)Developed a binary classification model using algorithms such as Logistic Regression, SVC to predict whether a customer will default on credit card payments.
• 2)Extraction of features from the customers dataset followed by exploratory data
• analysis, and also performed Visualizations using matplotlib library.
• 3)Performed Logistic Regression, SVC on our dataset to predict the Customer default
• rate, and also carried out Hyper-Parameter Tuning.
• 4)logistic regression algorithm was able to predict customer default on credit card with
• Recall of 82%, It will help the Business to reduce False-Negative rate.

Oct 2021 – Nov 2021 · 1 mo · Banglore

• 1)Developed a Regression model using algorithms such as Linear, Ridge , Lasso to
• predict the Number of Tickets that would be sold in each ride.
• 2)Employed processing techniques such as feature scaling, outlier treatment, duplicate
• entries and aslo performed missing value imputation to generate train and test data.
• 3)Performed temporal cross-validation to avoid any information leakage while training
• and experimenting with hyperparameter tuning techniques.
• 4)Here XGboost with tuned hyper-parameter gave the best Result which has 79% R2
• score, This result help's the Transport-business to give waiting time to their customer

Sep 2021 – Aug 2022 · 11 mos · Banglore

• 1) Python-Programming
• 2) Excel, Tableau, SQL
• 3) Mathematics & Statistics for Data Science
• 4) Machine Learning
• 5) Industry Insights
• 6) ML& Data-Engineering
• 7) Deep Learning

Jun 2021 – Jul 2021 · 1 mo · Udupi, Karnataka, India

• This project should be taken only as a Linear Regression project and not as a Investing tool, because Stock price can never be actually predicted, It is determined by lot of factors, This project is only for understanding purpose.

Jun 2019 – Aug 2021 · 2 yrs 2 mos · Udupi, Karnataka, India

• Developed multiple Price-action Strategies in the Indian Stock-market, and Trained more than 500 Stock Trading Enthusiast.
• 1)Excellent ability to determine when to place purchase and sell orders to brokers.
• 2) Strong understanding of financial regulation markets, and stock Exchange.
• successfully created a supervised machine learning project for stock predictions.

Jun 2021 – Aug 2021 · 2 mos · Banglore

• I started my Stock trading journey at Havenspire pvt.limited. It's one of the India's Largest stock trading Community. later I worked as a Mentor in the same organisation. where I learnt so many key skills such as :-
• 1) Investing
• 2) Value investing
• 3) Equity Trading
• 4) Future and Option trading
• 5) Stock Options
• 6) Strategies related to price action and indicators
• 7) Chart-analysis