Bharath

Enthusiastic Data Science graduate skilled in Python, SQL, Machine Learning, Deep Learning, and NLP with strong foundations in EDA, statistics , and model building. Completed practical projects such as Sentiment Analysis and a complete ML pipeline covering cleaning, preprocessing, exploratory analysis, model training, and performance evaluation. Previously managed end-to-end operations as a business owner , developing strong analytical and problem-solving skills. Looking for opportunities as a Data Analyst, ML Engineer, or Data Scientist to apply technical learning in real-world scenarios.

• Domain: Machine Learning & Analytics
• Built a real-time, multi-model system on police crash data to identify high-risk zones, injury severity, and accident patterns using classification, clustering, and EDA.

• Domain: Regression & Visualization
• Engineered customer behavior metrics and applied regression modeling to segment policyholders into actionable value tiers for personalized service allocation.

• Domain: Clustering in Insurance
• Performed behavioral clustering on 9,000+ credit card customers using K-Means and PCA to identify actionable customer segments for targeted marketing strategies.

• Domain: Recommendation & Agriculture
• Designed a data-driven crop recommendation system to assist farmers in selecting suitable crops, aiming to improve yield and reduce resource wastage.

• Domain: Image Classification using CNN
• Developed a deep learning–based scene classification model to identify environmental contexts (urban vs natural) from images, supporting smart city and autonomous navigation use cases.

• Domain: E-Commerce & Neural Networks
• Designed a neural network–based dynamic pricing system that modeled purchase probability as a function of price, customer behavior, and demand signals to identify revenue-optimal pricing strategies.

• Domain: NLP & Text Sentiment Analysis
• Created a reusable prediction workflow with model persistence (pickle), ensuring consistent and scalable sentiment predictions on unseen data.

• Domain: Predictive Analysis & Banking
• Demonstrated end-to-end ML workflow skills, from data cleaning to model evaluation and interpretation, emphasizing practical deployment-ready pipeline design for industry-style projects.