Hi there, I'm Rohit Bele! 👋

About Me

I'm a passionate Data Science enthusiast with a knack for solving real-world problems through Machine Learning. My focus lies in leveraging the power of data to drive insights and innovations. Let's connect and collaborate!

🔬 Expertise

Machine Learning: Pandas, PyTorch, TensorFlow
Data Science: Exploratory Data Analysis, Predictive Modeling
Tools: Jupyter Notebooks, Scikit-learn, Matplotlib, Seaborn

🚀 Projects

Student Performance Prediction

Predicting student success using machine learning models and offering valuable insights. Check out the project here!

🏆 Achievements

Shortlisted Participant, Amazon ML Summer School 2023
3rd Position, Kaggle ML Olympiad 2024 Challenge
Founder & Author, TechPulse Newsletter

📫 Get in Touch

Feel free to reach out:

Email: [email protected]
LinkedIn: belerohit

Let's collaborate and innovate together! 💡

Mr Bele Rohit's Projects

belerohit

Config files for my GitHub profile.

cap-trial-1

The project is a machine predictive maintenance application that uses machine learning (Random Forest) to classify whether a machine will experience failure or not based on various input parameters.

carprice_prediction_using_mlr_vif_rfe

In this project, I've delved into the exciting world of data-driven car price prediction, leveraging the power of machine learning techniques to create a robust model that estimates car prices with impressive accuracy.

chatgpt-jailbreaks

Official jailbreak for ChatGPT (GPT-3.5). Send a long message at the start of the conversation with ChatGPT to get offensive, unethical, aggressive, human-like answers in English and Italian.

codechefodyssey75

🚀 Join my coding journey! Daily solutions to CodeChef problems. 75 days, 75 challenges. Let's level up together! 💻🔥 #CodeChef #CodingJourney #75DaysChallenge

freecodecamp

This is the repo where I have stored all the projects related to freecodecamp's data visualization certification program.

movie_reccomendation_system

My Recommendation System project is an implementation of a collaborative filtering algorithm that recommends items to users based on their historical preferences. The algorithm was built using Python and various libraries such as NumPy, Pandas, and Scikit-learn.

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