Welcome to my Machine Learning Projects Repository! This repository showcases a collection of machine learning projects covering various domains. Each project demonstrates different techniques, tools, and datasets used in the field of machine learning.
Explore a set of diverse machine learning projects in this repository. Each project aims to solve unique problems using different algorithms and datasets.
- Description: Predict house prices using a regression model.
- Features: Regression techniques, data preprocessing, model evaluation.
- Dataset: "HousePricePrediction.csv"
- Key Insights: Explored feature importance, identified factors affecting house prices, achieved an R-squared score of 0.85.
- Description: Classify iris flowers using machine learning algorithms.
- Features: Classification algorithms, data visualization, model evaluation.
- Dataset: "iris.csv"
- Key Insights: Achieved high accuracy with the Random Forest classifier, visualized species distribution using Seaborn.
- Description: Predict wine quality based on key features.
- Features: Regression techniques, feature scaling, cross-validation.
- Dataset: "winequality-red.csv"
- Key Insights: Discovered positive correlation between alcohol content and quality, volatile acidity's negative impact on quality, achieved 75% accuracy.
To explore these projects:
- Clone this repository to your local machine.
- Navigate to individual project folders for detailed code, explanations, and results.
- Refer to each project's README for specific setup instructions and usage details.
Feel free to use or modify the code for your learning and projects. Follow the README instructions in each project to run the code and explore the outcomes.
If you'd like to contribute, open a pull request. Contributions can include new projects, improvements, or bug fixes.
Connect with me on LinkedIn to discuss these projects and more! Let's share our passion for machine learning. #MachineLearning #DataScience #ProjectsRepository #LearningJourney