About

Ram Bevara

Hi, I'm Rambabu Bevara, a passionate Data Analyst & Machine Learning Enthusiast who loves turning raw data into clear, actionable insights.

I work with Python, SQL, Power BI, and Excel to clean, analyze and visualize data. On the machine learning side, I build models for regression, classification, recommendation systems, NLP, and computer vision.

Some of my key projects include House Price Prediction, Beauty Product Recommendation, Remote Worker Mental Health Prediction, Sentiment Analysis on Clothing Reviews, and Image-based Similarity & Object Detection.

I enjoy learning by building end-to-end projects – from data collection and EDA to model deployment on Streamlit. I’m actively looking for opportunities as a Data Analyst / Junior Data Scientist.

Projects

Real Estate by Weather & Disaster EDA
Real Estate by Weather & Disaster – EDA

Exploratory Data Analysis on real estate data combined with weather and disaster-related factors, finding patterns that affect property demand and pricing.

Source Code
Beauty Product Recommendation
Beauty Product Recommendation System

Hybrid recommendation engine using TF-IDF, cosine similarity, and user preferences to recommend relevant beauty products.

App Link Source Code
Mental Health Prediction
Remote Worker Mental Health Prediction

Machine learning model that predicts potential mental health risk for remote workers using survey-based features.

App Link Source Code
House Price Prediction
House Price Prediction

Regression model built using ensemble learning (Random Forest) to predict house prices with strong R² performance.

Source Code
Sentiment Analysis
Sentiment Analysis – Clothing Reviews

NLP pipeline to classify customer reviews into sentiment classes using TF-IDF features and ML models.

Source Code
Tribe Image Classification
Tribe Image Classification

Deep learning image classifier to identify different tribes / cultural groups using CNN-based models and transfer learning on image datasets.

Source Code
Footwear Similarity
Footwear Similar Image Retrieval

Computer vision project using CNN embeddings and cosine similarity to retrieve visually similar footwear images.

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Necklace Object Detection
Necklace Type Object Detection

Object detection pipeline using deep learning to detect and classify necklace types with bounding boxes.

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Skills Covered

Python

Core programming language for all data science workflows including data analysis, automation, and machine learning model development.

Machine Learning

Experience building predictive models using regression, classification, clustering, and ensemble methods with Scikit-learn.

Deep Learning

Hands-on experience with neural networks, CNNs, and RNNs using TensorFlow and PyTorch for AI applications.

Natural Language Processing

Implemented NLP models for sentiment analysis, summarization, and query based text generation using BERT and BART.

Generative AI

Explored LLMs, prompt engineering, and fine tuning using Hugging Face and OpenAI APIs to build intelligent applications.

Power BI

Developed interactive dashboards to visualize key metrics and business insights for data driven decision making.

MySQL

Skilled in database creation, SQL queries, joins, and integrating data pipelines with Python and analytics tools.

Excel

Strong command of Excel for data analysis, pivot tables, Power Query, and quick visual insights.

Data Cleaning

Processed and transformed messy data into structured datasets using Python libraries like Pandas and NumPy.

Statistics & Probability

Applied statistical concepts like distributions, correlation, and hypothesis testing for analytical insights.

Exploratory Data Analysis

Performed univariate, bivariate, and multivariate analysis to uncover patterns and data insights.

Data Visualization

Created advanced visualizations using Matplotlib, Seaborn, Plotly, and Power BI for impactful storytelling.

Critical Thinking & Problem Solving

Used analytical reasoning to identify data driven solutions and optimize business or model outcomes.

Model Evaluation

Evaluated models using metrics like RMSE, ROC AUC, Precision, Recall, and F1-Score to ensure high accuracy.

Feature Engineering

Transformed and created features to boost model performance and extract more predictive insights.

Model Deployment

Deployed models using Streamlit locally and on the cloud for real time accessibility and interactivity.

Data Engineering

Built ETL pipelines, automated data workflows, and integrated structured and unstructured data sources.

Data Storytelling

Turned complex datasets into actionable stories using visual reports and dashboard presentations.

Web Scraping

Collected and analyzed large scale data using BeautifulSoup and Selenium for insights and automation.

Cloud Computing

The ability to deploy, scale, and manage machine learning models and data pipelines on cloud platforms essential for real world, production level data science solutions.

Contact Me

rambabubevara004@gmail.com

+91-9391898634