Siddardha Annamneedi
Visakhapatnam
Summary
Results-driven data professional with a strong foundation in data science and data analytics, skilled in SQL, Python, and advanced techniques in data visualization and modeling.
Overview
1
1
year of professional experience
1
1
Certification
Work History
Analyst
LatentView Analytics
Chennai
11.2024 - Current
- Performed Retention Analysis and Customer Segmentation for a U.S.‑based client, ensuring secure and accurate handling of sensitive information.
- Utilized advanced SQL techniques to transform and optimize datasets for faster analysis and reporting.
- Performed comprehensive exploratory data analysis (EDA) using Python to identify trends, outliers, and KPIs contributing to improvement in dashboard clarity and usability.
- Built and tuned a Random Forest classifier to predict customer age groups, driving targeted retention initiatives, and lowering churn risk.
- Performed feature engineering and selection that enhanced model accuracy and stability.
- Addressed severe class imbalance by generating synthetic samples with CTGAN, lifting minority‑class representation by 65 % and overall accuracy by 14 %.
- Validated models with PR-AUC, ROC-AUC, F1 score, and 10-fold cross-validation to ensure robust, reliable results.
- Designed and published an interactive Power BI dashboard that delivered actionable insights to stakeholders in real time.
- Tools & Tech: SQL, Python, Machine Learning, Deep Learning, Power BI.
Education
B.Tech - Computer Science & Engineering (CSE)
Visvesvaraya National Institute of Technology
NIT Nagpur05-2024
Skills
- SQL
- Python
- Machine Learning (ML)
- Deep Learning (DL)
- Natural Language Processing (NLP)
- Business Intelligence
- Power BI
- Looker
Certification
- Microsoft Certified: Power BI Data Analyst Associate
Projects
Image categorizer
- Built a CNN-based image classification model with over 92% accuracy across 10 image classes, enabling efficient categorization in real-time applications
- Reduced dataset size by 40% using pruning and stratified sampling, cutting training time without compromising accuracy
- Tools and Tech: Python, TensorFlow, scikit-learn, NumPy, Pandas, Matplotlib, and Seaborn
NLP chatbot
- Collected and cleaned historical customer support chat logs, performed text normalization, tokenization, stop-word removal, and lemmatization.
- Implemented intent classification using a supervised machine learning model and entity recognition using spaCy.
- Trained and evaluated various NLP models, including TF-IDF, ML classifiers, and transformer-based models (BERT), to improve intent recognition accuracy.
- Developed a rule-based and retrieval-based response engine to handle context-aware conversations and fallback mechanisms.
- Measured chatbot performance using metrics such as intent accuracy, F1-score, and response relevance through manual testing and user feedback.
- Tools & Technologies: Python, NLTK, SpaCy, Scikit-learn, TensorFlow, Transformers (HuggingFace), Rasa
Timeline
Analyst
LatentView Analytics
11.2024 - Current
B.Tech - Computer Science & Engineering (CSE)
Visvesvaraya National Institute of Technology
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