AHIJIT KANTE

Highly motivated Computer Science Engineering undergraduate who specializes in Artificial Intelligence, Machine Learning and Web Development. I have done some hands-on projects that helped me learn more about Machine Learning algorithms.

Password Manager | project |Source Code

• Created a password manager application using React, Tailwind CSS, MongoDB, and Express.
• Demonstrated various aspects of application development including UI design, state management, form handling, and backend setup.

Tech used :- React, Tailwind CSS,Express, MongoDB,React Router, Mongoose and Yup.

Interactive Portfolio Website | project |Source Code

• Demonstrated proficiency in front-end development (HTML, CSS, JavaScript).
• Implemented responsive design practices for optimal viewing on any device.
• Provided contact information and links to online profiles.

Tech used:-HTML, CSS, JavaScript and Responsive Design.

Real Estate Price Prediction Model with Machine Learning | project |Source Code

• Leveraged machine learning to build a model that estimates property values based on specific features.
• Ensured data quality by addressing missing values, inconsistencies, and outliers for accurate model training.
• Ensured data quality by addressing missing values, inconsistencies, and outliers for accurate model training.
• Explored various models (likely focusing on linear regression) and evaluated their performance to ensure reliable price predictions.
• Tech used:-Python,Pandas, Scikit-learn,HTML, CSS, JavaScript and Matplotlib.

Image Classification Project| project | Source Code

• Explored various methods for acquiring image data, including manual download, Python scripting, and pre-existing datasets.
• Performed data cleaning tasks using OpenCV for face and eye detection to ensure data quality and model accuracy.
• Implemented feature engineering techniques like wavelet transforms to extract relevant features from images, enhancing model performance.
• Built and trained a machine learning model (likely Support Vector Machine) for image classification.
• Evaluated the model's performance using metrics like precision, recall, and F1-score. Compared different models (e.g., SVM, logistic regression) to determine the best option.
• Tech used:- Python,OpenCV,scikit-learn,HTML, CSS and JavaScript