Deep Learning Model that classifies brain tumor from images

Google Collab for Testing AnythingV3.0 Diffusion Model

The IPL EDA (Exploratory Data Analysis) was conducted, revealing valuable insights. The analysis focused on various aspects such as player performance, team statistics, and match outcomes. Key findings include trends in run-scoring, top performers, and team dynamics. The EDA offers actionable insights for teams and fans to make data-driven decision

The project investigates the effectiveness of different machine learning models in predicting sales. XGBoost Regression with tuned hyperparameters achieved the best performance among the tested models based on the Mean Squared Error metric.

Kumpulan Dasar Pemrograman Python Studi Independen.

Through machine learning, we can predict the stock projections for the selected companies

The Whisper Hindi ASR (Automatic Speech Recognition) model utilizes the KathBath dataset, a comprehensive collection of speech samples in Hindi. Trained on this dataset, Whisper employs advanced deep learning techniques to accurately transcribe spoken Hindi into text.

The project or work which goals to extract the opinions, emotions, attitudes of public towards different object of interest. Sentiment analysis is a form of shallow semantic analysis of texts. In the project an automatic approach that involves supervised machine learning and text mining classification algorithms are used which includes the senti…

Classifying the following 5 types of flowers: Rose, Daisy, Dandelion, Sunflower and Tulip

I will be implementing a machine learning model that predicts the fare for a cab ride based on the data collected from previous rides .

Labs, control works and other materials that were created during university studies

Based on Image Classification, this is a machine model for classifying cats and dogs. The model was created using Google Collab and has an accuracy of 85%. The model is in the "saved models" folder, and the notebook is in the "training notebooks" folder.

In this project a convolutional neural network (CNN) is trained to learn the behavior of a car using data from a simulator that allows real-time information gathering from the car’s chassis, position and its speed. As a first step the vehicle is driven in a manual mode of simulation for collecting data. Then the neural network uses information f…

Classification of Spam SMS/ Emails based on Naïve Bayes Classifier

This project explores the optimal combination of Bag-of-Words and TF-IDF vectorization with Naive Bayes and SVM for sentiment analysis. It evaluates performance using accuracy, precision, recall, and F1-score, addressing ethical concerns like data privacy and bias to improve sentiment classification in real-world applications.

A web scraper using Python and Beautiful Soap for review extraction from IMDb.

📊 Using machine learning to train a model using soft max regression based on real twitter datasets to classify vaccine sentiments to Pro-vax, Anti-vax and Neutral.

Built and evaluated machine learning model to solve stroke problem using Stroke dataset.

This project leverages machine learning techniques to predict the future prices of Microsoft's stock. By analyzing historical stock price data, the model aims to provide accurate predictions that can be used to make informed investment decisions.

Kaggle competition, classification image with the basic data set MNIST