Tejas Kulal Email & Phone Number

Pune, Maharashtra, India

As an AI/ML Engineer Intern, I work on various machine learning projects focused on building predictive models and optimizing algorithms for real-world applications. My responsibilities include data preprocessing, feature engineering, and developing models using Python and machine learning frameworks like scikit-learn. I collaborate with teams to solve complex problems using AI techniques, including computer vision and NLP, while also contributing to data visualization and reporting using Power… Show more As an AI/ML Engineer Intern, I work on various machine learning projects focused on building predictive models and optimizing algorithms for real-world applications. My responsibilities include data preprocessing, feature engineering, and developing models using Python and machine learning frameworks like scikit-learn. I collaborate with teams to solve complex problems using AI techniques, including computer vision and NLP, while also contributing to data visualization and reporting using Power BI. This internship has allowed me to apply my skills in a practical environment and contribute to innovative AI-driven solutions. Show less

Magarpatta It Park, Hadapsar-28 Pune.

As part of my internship at Puneri Pattern Pvt Ltd, I worked on predicting the closing stock prices of Yes Bank using machine learning regression models. The project involved multiple stages, from data cleaning and feature engineering to model implementation and evaluation. Here’s a summary of the approach taken:Exploratory Data Analysis (EDA): Visualized and analyzed the dataset to gain insights, identify patterns, and address data quality issues such as missing values, duplicates, and… Show more As part of my internship at Puneri Pattern Pvt Ltd, I worked on predicting the closing stock prices of Yes Bank using machine learning regression models. The project involved multiple stages, from data cleaning and feature engineering to model implementation and evaluation. Here’s a summary of the approach taken:Exploratory Data Analysis (EDA): Visualized and analyzed the dataset to gain insights, identify patterns, and address data quality issues such as missing values, duplicates, and outliers.Data Scrubbing: Cleaned the data by removing anomalies, standardizing features, and normalizing values to ensure consistency and improve model performance.Feature Engineering: Created new features, such as Year, Month, and Quarter, to enhance model accuracy.Model Implementation: Used multiple regression models, including Linear Regression, Random Forest Regression, and Gradient Boosting Regression, to predict the closing stock prices. Each model's performance was evaluated using metrics like MSE, MAE, and R², and cross-validation was performed for robustness. The Random Forest Regression model was selected as the optimal one for implementation.Model Explainability: Utilized SHAP (SHapley Additive exPlanations) to understand the factors influencing stock price predictions, highlighting feature importance and the relationship between features and predictions.Business Impact: This project can help investors and financial institutions make smarter, data-driven investment decisions, better manage risks, and gain a competitive edge in the market by predicting Yes Bank stock price trends accurately. Show less

Bachelor Of Engineering - Be, Electronics And Telecommunication, 7.25

Education record