This repository contains various data science projects, each focusing on different aspects of machine learning and data analysis. Below is a brief overview of each notebook and how to get started.
- Description: Predicts acidity levels in wine based on various features using machine learning algorithms.
- File:
Acidity_Prediction.ipynb
- Description: Predicts housing prices in California using a dataset and regression models.
- File:
California_Housing_Price_Prediction.ipynb
- Description: Predicts the likelihood of diabetes based on patient data using classification algorithms.
- File:
Diabetes_Prediction.ipynb
- Description: Classifies handwritten digits from the MNIST dataset using machine learning models.
- File:
Digit_Classification.ipynb
- Description: Analyzes Instagram user data to gain insights into user behavior and engagement.
- File:
Instagram_User_Analytics.ipynb
- Description: Classifies iris flower species based on petal and sepal measurements using classification techniques.
- File:
Iris_Classification.ipynb
- Description: Performs Named Entity Recognition on text data using NLTK.
- File:
Named_Entity_Recognition_(NER)_with_NLTK.ipynb
- Description: Classifies penguin species based on various features using machine learning algorithms.
- File:
Penguin_Classification.ipynb
- Description: Analyzes power consumption data to identify trends and make predictions.
- File:
Power_Consumptions.ipynb
- Description: Predicts survival chances of passengers on the Titanic using classification models.
- File:
Titanic.ipynb
- Description: Classifies wine quality based on chemical properties using machine learning algorithms.
- File:
Wine_Quality_Classification.ipynb
- Description: Classifies news articles into different categories using text classification techniques.
- File:
text_classification_for_news_articles.ipynb
-
Clone the Repository:
git clone https://github.com/yourusername/repositoryname.git
-
Navigate to the Project Directory:
cd repositoryname
-
**Install Required Packages: **
You may need to install the necessary Python libraries.
You can do this using pip:
pip install -r requirements.txt
-
Run a Jupyter Notebook:
Start Jupyter Notebook and open the desired notebook file:
jupyter notebook
This repository is licensed under the MIT License. See the LICENSE file for more information.
If you would like to contribute to this repository, please open an issue or submit a pull request.
For any questions or feedback, please contact goutamhegde2000g@gmail.com.