https://github.com/surajsanap/technohack_mlinternship
1) Wine Quality Analysis and Classification, 2)Movie Review Sentiment Analysis, 3)Diabetes Prediction Using Machine Learning
https://github.com/surajsanap/technohack_mlinternship
deep-learning machine-learning pandas python scikit-learn
Last synced: about 1 year ago
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1) Wine Quality Analysis and Classification, 2)Movie Review Sentiment Analysis, 3)Diabetes Prediction Using Machine Learning
- Host: GitHub
- URL: https://github.com/surajsanap/technohack_mlinternship
- Owner: SurajSanap
- License: mit
- Created: 2023-09-18T05:17:02.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-18T05:20:55.000Z (almost 3 years ago)
- Last Synced: 2025-05-08T19:13:06.024Z (about 1 year ago)
- Topics: deep-learning, machine-learning, pandas, python, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Machine Learing Models
# Wine Quality Analysis and Classification
This project involves the analysis and classification of wine quality using Python. It utilizes popular libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.
# Movie Review Sentiment Analysis
This repository contains Python code for performing sentiment analysis on movie reviews using deep learning techniques. It demonstrates how to preprocess text data, create a deep learning model, and make predictions on test data.
# Diabetes Prediction Using Machine Learning
This repository contains a Jupyter Notebook named "Diabetes Pred task 1.ipynb" for a machine learning project focused on predicting diabetes in patients based on various features. The project utilizes the Pima Indians Diabetes Database.
## Getting Started
To run this project, you will need to have Python and the following libraries installed:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
You can install these libraries using pip:
```bash
pip install numpy pandas matplotlib seaborn scikit-learn
## Preprocessing
Text preprocessing is a crucial step in sentiment analysis. In the code, text data is preprocessed by:
- Removing HTML tags
- Removing URLs
- Removing special characters and punctuation
- Tokenizing the text
- Removing emojis
- Cleaning non-alphabetical characters
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.