Hi, I'm
Madhumitha Reddy
A Mern Stack Developer.
A Problem Solver.
Exploring Machine Learning.
Go! Check out my projects!
Hi, I'm
A Mern Stack Developer.
A Problem Solver.
Exploring Machine Learning.
Go! Check out my projects!
Hello, I'm Madhumitha Reddy, a passionate and dedicated Computer Science student from Warangal, Telangana. I enjoy building practical web applications and continuously learning modern tools that help me solve real-world problems.
My journey in technology has shaped my problem-solving mindset, communication, and adaptability. I’m focused on creating scalable and user-friendly products while exploring opportunities that improve both technical and professional growth.
An AI-powered travel planning app that generates personalised itineraries using Google Gemini AI, with OAuth authentication and intelligent place suggestions for trip creation.
This project focuses on classifying iris flowers into three species — Setosa, Versicolor, and Virginica — using machine learning techniques. The model is trained on the famous Iris dataset, which includes features such as sepal length, sepal width, petal length, and petal width. Data visualization and preprocessing are performed to understand the dataset better. Various classification algorithms are applied to predict the flower species accurately. This project demonstrates the basics of supervised learning and classification in machine learning.
This project focuses on detecting spam emails using machine learning techniques. The dataset is preprocessed using text cleaning and feature extraction methods such as TF-IDF or Count Vectorization. Different classification algorithms are applied to classify emails as spam or ham (not spam). The model is trained and evaluated to improve prediction accuracy. This system helps in automatically filtering unwanted emails and improving email security and efficiency.
This project focuses on predicting the price of cars using machine learning techniques. The model is trained on a dataset containing different car features such as brand, model, year, mileage, and engine specifications to estimate the market price of a vehicle. Data preprocessing, exploratory data analysis, and regression algorithms are used to build the predictive model. The goal of this project is to provide accurate price predictions that can help buyers and sellers make better decisions in the automobile market.
Received certification for completing Problem Solving assessment.
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Completed advanced graph theory algorithms and applications in competitive programming.
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Participated in The DevForge hackathon organized by Scaler School of Technology, Bengaluru, Karnataka.
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