This repository contains machine learning projects implemented using Colab notebooks. Projects cover various algorithms and techniques including linear regression, logistic regression, support vector machine, and dimensionality reduction.
Welcome to the Machine Learning Projects repository! This collection of projects is designed to provide hands-on experience with various machine learning algorithms and techniques using Google Colab notebooks. Whether you're a beginner looking to understand fundamental and metric aspects, these projects offer a practical approach to learning and implementing machine learning.
Each project focuses on a specific algorithm or aspect of machine learning, allowing you to dive deep into its theory, implementation, and real-world applications. From simple linear regression to sophisticated support vector machines and dimensionality reduction techniques, there's something here for every level of expertise.
The projects are implemented using Python and popular libraries such as NumPy, pandas, scikit-learn, and matplotlib. They are structured in a way that encourages exploration and experimentation, with detailed explanations and code comments to guide you through the process. Additionally, the use of Google Colab provides a seamless environment for running code, visualizing results, and collaborating with others in real-time.
List the machine learning projects included in this repository. Provide a short description of each project and link to the corresponding Colab notebook.
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Linear Regression
- This project implements linear regression to predict house prices using linear regression. This project leverages scikit-learn to build and evaluate a model based on features like size, bedrooms, and location, aiming to accurately estimate house prices.
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Logistic Regression
- This project utilizes logistic regression to predict health insurance customer demographics. By analyzing features such as age, gender, BMI, and region, the model aims to classify customers into demographic categories, aiding in targeted marketing strategies and personalized services.
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Support Vector Machine
- This project employs Support Vector Machines (SVM) for sentiment analysis. By training on labeled textual data, the SVM model predicts sentiment (positive/negative) from text inputs, enabling automated analysis of sentiments in various contexts such as reviews, social media posts, or customer feedback.
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Dimensionality Reduction
- This project explores word embeddings using dimensionality reduction techniques such as PCA and t-SNE. By visualizing high-dimensional word embeddings in a lower-dimensional space, we gain insights into semantic relationships between words, aiding in tasks like natural language processing and understanding word semantics.