This repository contains various machine learning projects that demonstrate different approaches and techniques in the field of machine learning. Each project focuses on solving specific problems and showcases the application of different machine learning models and algorithms. Below is an overview of the projects included in this repository:

This notebook focuses on the prediction of house prices using regression models. The dataset used is the Boston Housing dataset, which contains information about various features of houses and their corresponding prices. The notebook explores different regression models and evaluates their performance in predicting house prices.

The ASL_letters.ipynb notebook deals with sign language recognition using the American Sign Language (ASL) alphabet. The dataset used in this project is derived from the MNIST sign language dataset. It consists of 27,455 grayscale images of size 28x28 pixels, representing the 26 letters of the English alphabet (excluding J and Z).

The notebook demonstrates the use of TensorFlow and utilizes data streaming generators to preprocess and load the dataset. The images are organized in folders corresponding to their respective classes, facilitating easy loading and visualization. The ASL_letters.ipynb notebook explores different machine learning approaches to classify the sign language images accurately.

This notebook presents an analysis of a sentiment classification model using the IMDB database. The objective is to build a neural network model that can predict whether a movie review is positive or negative. The notebook explores various preprocessing techniques and evaluates the performance of different models in classifying sentiments.

The Multiple_Classification-of-News.ipynb notebook focuses on classifying news articles into their respective categories using a neural network. The notebook employs advanced machine learning techniques to analyze the content and characteristics of each news piece for accurate classification. By leveraging neural networks, the notebook demonstrates the power of deep learning in automating the categorization process and achieving high classification accuracy.

This notebook performs an analysis of a dataset related to diabetes. It explores various data exploration and visualization tasks to uncover patterns and relationships within the dataset. Additionally, the notebook builds a prediction model using logistic regression to classify patients based on their medical characteristics and predict whether they have diabetes or not.

In this project, an image recognition model is developed using TensorFlow for classifying garments in the Fashion MNIST dataset. The dataset consists of 60,000 grayscale images of 10 different categories. The notebook demonstrates the process of training a machine learning model on image data and showcases the accuracy of the developed model in classifying garments.

The Dogs-vs-cats.ipynb notebook aims to build a convolutional neural network (CNN) model capable of distinguishing between dog and cat images. By utilizing the TensorFlow library, the notebook leverages the power of CNNs for image classification tasks. The notebook demonstrates how CNNs can effectively learn hierarchical representations and accurately classify images based on their content.

Each project folder in this repository contains the respective notebook along with any necessary datasets or resources. Feel free to explore and learn from these projects to enhance your understanding of machine learning and its various applications.