Dipti Nayak

B.Sc Computer Science student with strong foundation in data analytics, Machine learning, and statistical modeling. Proficient in Python, SQL, Pandas, NumPy, Scikit-learn, and Power B. Experienced in data preprocessing, building classification and regression models. Completed academic projects involving data cleaning, exploratory data analysis (EDA), and predictive analytics. Knowledgeable in Excel, data visualization, and Git version control. Strong problem-solving and critical thinking skills. Eager to contribute to real world data science projects in a collaborative environment.

• RNA 3D Structure Prediction Using Machine Learning , Python, PyTorch, MLP, Bioinformatics

Developed a machine learning pipeline to predict 3D atomic coordinates of RNA sequences. Implemented data preprocessing with one-hot encoding, trained a Multi-Layer Perceptron (MLP) model using PyTorch, and evaluated performance using RMSE metrics. The project demonstrates proficiency in applying deep learning techniques to bioinformatics challenges.

Key Skills: Python, PyTorch, Machine Learning, Bioinformatics, Data Preprocessing, Model Evaluation

GitHub Repository: github.com/nayakdipti001/rna-3d-predictions

• Sales Dashboard & Forecasting Web App

Python, Streamlit, Pandas, Matplotlib, Seaborn

Built a dynamic Streamlit web app for analyzing and forecasting retail sales. Enabled CSV uploads (train.csv, store.csv) and provided interactive filtering by store, year, and month. Visualized key sales metrics and trends through custom plots, and allowed users to download filtered datasets. Focused on user experience, data preprocessing, and clear trend analysis to support business decision-making.

Key Skills: Streamlit, Python, Data Visualization, Sales Forecasting, Pandas, Matplotlib, CSV Processing

Live App: projects-lzdkq2knihfpayqrnuytdj.streamlit.app

• Flappy Bird AI – Game Agent using NEAT Algorithm

Python, PyGame, NEAT-Python

Created an AI agent to autonomously play the Flappy Bird game using the NEAT (NeuroEvolution of Augmenting Topologies) algorithm. Implemented the game mechanics in PyGame and designed a fitness function to guide neural network evolution across generations. Visualized agent learning progression and performance metrics to track improvements in gameplay behavior.

Key Skills: Python, NEAT, PyGame, Neural Networks, Evolutionary Algorithms, Game Development

GitHub: github.com/nayakdipti001/FlappyBirdAI