MCA student passionate about using data, Python, and machine learning to solve real-world problems and build data-driven solutions.
From cleaning messy datasets to shipping trained models — here's where I focus.
Turning raw data into dashboards and reports using Power BI, Tableau, Excel and SQL that drive real decisions.
Building, tuning and deploying machine learning models with Scikit-learn, TensorFlow, Keras and Flask.
Structuring analysis and visuals so technical and non-technical stakeholders reach the same conclusion, fast.
I'm Dharani Kaniti, an MCA student at SRK Institute of Technology, Vijayawada, with a strong interest in data analytics, Python development, and machine learning. I enjoy transforming raw data into meaningful insights and building practical technology solutions for real-world problems.
My experience includes data analysis, dashboard development, machine learning model development, and applied deep learning — with Python, SQL, Power BI, Tableau, Excel, Scikit-learn, TensorFlow, Keras, Flask, and Django.
Data Analyst at Codegnan · Python/ML at VXL IT Solutions
LLMs, RAG, and vector databases — building toward well-rounded, data-driven work
Explore, clean and validate the dataset before drawing any conclusions.
Engineer features, train models, and tune them for measurable accuracy gains.
Ship dashboards or deployed APIs that people actually use to decide.
Detection accuracy achieved on the Deepfake Detection deep-learning project using CNN/Xception.
Reduction in reporting time delivered through interactive Power BI/Tableau dashboards at Codegnan.
Prediction accuracy on the Loan Eligibility system, improved 15–20% through hyperparameter tuning.
A selection of applied ML and analytics work — from computer vision to network anomaly detection.
Detects real vs. manipulated images/video using CNN/Xception with face detection and data augmentation.
Evaluates applicant profiles on income, credit history and loan amount, tuned for a 15–20% lift.
Predicts salary ranges by experience, education and role using Gradient Boosting, deployed via Flask.
Flags anomalies in telecom RAN/CORE data using LSTM/CNN models, deployed as a Flask API.
I am an entry-level technology professional looking for opportunities where I can apply my Python, SQL, data analysis, and machine learning skills to solve real-world problems.
Also actively exploring LLMs, RAG, vector databases, and Generative AI.
I'm open to entry-level opportunities, collaborations, and conversations around data analytics, Python, machine learning, and emerging technologies.