Siddhanth Garg

Aspiring Data Scientist | Continuous Learner

siddhanth.gargsg@gmail.com +91-9310425107

Delhi, India leetcode.com/siddhanth_1919/

linkedin.com/in/siddhanth-garg-7b13b0232 github.com/siddhanth19

www.hackerrank.com/siddhanth1919?hr_r=1

EDUCATION TECHNICAL SKILLS

B.Tech. - Computer Science and Engineering Python MySQL C++ C JAVA
Vellore Institute of Technology (CGPA 9.24 )
Power BI
09/2021 - Present, Vellore, Tamil Nadu

XII
Central Board of Secondary Education (88.8%) SKILLS AND ACHIEVEMENTS
08/2019 - 07/2021, Delhi
Problem Solving and Coding
Successfully solved over 150 intricate coding challenges on LeetCode,
X showcasing a persistent commitment to enhancing algorithmic
Central Board of Secondary Education (82%) proficiency.
10/2017 - 05/2019, Delhi Programming Proficiency
Achieved a remarkable 5-star Gold rank in C++ proficiency on
HackerRank, attesting to a high level of programming competence and
language mastery.
PERSONAL PROJECTS
Technical Problem Solving
Spam Email Classifier (Python) (2023) Demonstrated exceptional aptitude by earning a notable 4-star Silver
In this project, I conceptualized and executed a robust spam email rank in problem solving on HackerRank, indicative of an ability to tackle
classifier using Python. Employing the CountVectorizer technique diverse technical issues with effective solutions.
for text preprocessing and feature extraction, I transformed raw
email content into a format conducive to analysis. Natural Language Processing (NLP)
By utilizing the Naive Bayes algorithm, the model achieved an Proficient in using NLP techniques for text preprocessing, feature
impressive accuracy rate of 98.11%. This undertaking demonstrated extraction, and sentiment analysis.
my proficiency in text classification, showcasing my ability to
effectively leverage NLP tools and machine learning algorithms for Python Programming
real-world challenges. Skilled in coding and developing applications using Python for data
manipulation, analysis, and machine learning.
MNIST Digit Classifier (Python) (2023)
Developing a MNIST digit classifier using Python, I harnessed the Dataset Preprocessing
power of logistic regression to differentiate between the ten Adept at preparing and structuring datasets, including normalization,
distinct digits (0-9). A meticulous approach to dataset feature engineering, and partitioning for training and testing.
preprocessing, including pixel value normalization and feature
vector conversion, contributed to accurate classification.
Machine Learning Classification and Regression
By employing k-fold cross-validation, I robustly evaluated the Capable of building robust classification models and accurate regression
model's performance, attaining an average cross-validation score models using algorithms like Naive Bayes, logistic regression, linear
surpassing 91%. Additionally, precision, recall, and F1 score analysis regression and ensemble methods.
revealed an impressive 97% precision rate, validating the model's
proficiency in digit recognition. Cross-Validation and Evaluation
Experienced in using cross-validation techniques to assess model
Sales Prediction Regression (Python) (2023) performance and employing evaluation metrics like precision, recall, and
Embarking on a data-driven journey, I engineered a comprehensive F1 score.
sales prediction model utilizing Python. Leveraging the capabilities
of the Random Forest Regressor algorithm, I achieved an Hyperparameter Tuning:
exceptional accuracy rate of 95.15%.
Competent in optimizing model performance by tuning hyperparameters
The successful application of hyperparameter tuning through Grid through techniques such as grid search and random search.
Search CV further underscored my proficiency in machine learning
techniques. This project showcased my ability to translate intricate
data into actionable insights, enabling accurate sales predictions.
The project involved meticulous data cleaning, preprocessing, and CERTIFICATES
thoughtful feature engineering to optimize model performance.
Kaggle - Intermediate Machine Learning (02/2023)
Exhaustion and Stress Level Detection (Ongoing)
Engaged in an ongoing project aimed at developing a Kaggle - Data Visualization (02/2023)
comprehensive exhaustion and stress level detection system using
Python. Leveraging models like Haar Cascade and Custom models Udemy - Ultimate Python Bootcamp For Data Science &
for facial analysis, the project will be designed to accurately identify
signs of stress and exhaustion.
Machine Learning (12/2022)
In addition, the project includes plans to integrate the detection SmartBridge - Artificial Intelligence and Machine
system with a user-friendly web application, facilitating real-time
monitoring and analysis of stress levels. Learning with Google (Onging)
Ethnus - AWS Architect Associate (Ongoing)