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Below is a brief overview of each notebook and how to get started.\n\n## Projects\n\n### 1. **Acidity Prediction**\n- **Description**: Predicts acidity levels in wine based on various features using machine learning algorithms.\n- **File**: `Acidity_Prediction.ipynb`\n\n### 2. **California Housing Price Prediction**\n- **Description**: Predicts housing prices in California using a dataset and regression models.\n- **File**: `California_Housing_Price_Prediction.ipynb`\n\n### 3. **Diabetes Prediction**\n- **Description**: Predicts the likelihood of diabetes based on patient data using classification algorithms.\n- **File**: `Diabetes_Prediction.ipynb`\n\n### 4. **Digit Classification**\n- **Description**: Classifies handwritten digits from the MNIST dataset using machine learning models.\n- **File**: `Digit_Classification.ipynb`\n\n### 5. **Instagram User Analytics**\n- **Description**: Analyzes Instagram user data to gain insights into user behavior and engagement.\n- **File**: `Instagram_User_Analytics.ipynb`\n\n### 6. **Iris Classification**\n- **Description**: Classifies iris flower species based on petal and sepal measurements using classification techniques.\n- **File**: `Iris_Classification.ipynb`\n\n### 7. **Named Entity Recognition (NER) with NLTK**\n- **Description**: Performs Named Entity Recognition on text data using NLTK.\n- **File**: `Named_Entity_Recognition_(NER)_with_NLTK.ipynb`\n\n### 8. **Penguin Classification**\n- **Description**: Classifies penguin species based on various features using machine learning algorithms.\n- **File**: `Penguin_Classification.ipynb`\n\n### 9. **Power Consumptions**\n- **Description**: Analyzes power consumption data to identify trends and make predictions.\n- **File**: `Power_Consumptions.ipynb`\n\n### 10. **Titanic Survival Prediction**\n- **Description**: Predicts survival chances of passengers on the Titanic using classification models.\n- **File**: `Titanic.ipynb`\n\n### 11. **Wine Quality Classification**\n- **Description**: Classifies wine quality based on chemical properties using machine learning algorithms.\n- **File**: `Wine_Quality_Classification.ipynb`\n\n### 12. **Text Classification for News Articles**\n- **Description**: Classifies news articles into different categories using text classification techniques.\n- **File**: `text_classification_for_news_articles.ipynb`\n\n## Getting Started\n\n1. **Clone the Repository**:\n   ```bash\n   git clone https://github.com/yourusername/repositoryname.git\n   ```\n2. **Navigate to the Project Directory:**\n\n   ```\n      cd repositoryname\n   ```\n\n3. **Install Required Packages: **\n\n      You may need to install the necessary Python libraries. \n\n      You can do this using pip:\n\n   ``` bash\n      pip install -r requirements.txt\n   ```\n\n4. **Run a Jupyter Notebook:**\n\n      Start Jupyter Notebook and open the desired notebook file:\n\n      ```\n      jupyter notebook\n      ```\n\n## License\n\nThis repository is licensed under the MIT License. See the LICENSE file for more information.\n\n## Contributing\n\nIf you would like to contribute to this repository, please open an issue or submit a pull request.\n\n## Contact\n\nFor any questions or feedback, please contact goutamhegde2000g@gmail.com.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoutamhegde002%2Fml-and-data-science-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgoutamhegde002%2Fml-and-data-science-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgoutamhegde002%2Fml-and-data-science-projects/lists"}