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https://github.com/kunalshelke90/wine-quality-testing
This project is about creating a machine learning algorithm that can predict the quality of wine based on the given dataset. Different machine learning algorithms such as logistic regression, decision tree and random forest are used in this project.
https://github.com/kunalshelke90/wine-quality-testing
eda feature-engineering flask machine-learning numpy pandas python
Last synced: 6 days ago
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This project is about creating a machine learning algorithm that can predict the quality of wine based on the given dataset. Different machine learning algorithms such as logistic regression, decision tree and random forest are used in this project.
- Host: GitHub
- URL: https://github.com/kunalshelke90/wine-quality-testing
- Owner: kunalshelke90
- License: mit
- Created: 2024-07-31T18:44:01.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T18:41:00.000Z (2 months ago)
- Last Synced: 2024-09-07T19:58:03.710Z (2 months ago)
- Topics: eda, feature-engineering, flask, machine-learning, numpy, pandas, python
- Language: Jupyter Notebook
- Homepage:
- Size: 288 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Wine Quality Prediction
This project focuses on predicting the quality of wines based on various physicochemical properties.The dataset used in this project is the "Wine Quality" dataset, which is publicly available. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.
# Dataset Link
https://archive.ics.uci.edu/dataset/186/wine+quality
## Run Locally
Clone the project
```bash
git clone https://github.com/kunalshelke90/Wine-Quality-Testing.git
```
Go to the project directory
```bash
cd Wine-Quality-Testing
```
Create a virtual environment and install dependencies:
```bash
conda create -p myenv python=3.8 -y
```
```bash
conda activate myenv
```
```bash
pip install -r requirements.txt
```
## Usage
1. Start the Flask application:
```bash
python app.py
```
2. Access the application:
Open your web browser and go to http://localhost:8080 to interact with the application. or http://127.0.0.1:8080
# Steps included in this project:
Importing Lib
Loading Data
Understanding Data
Missing Values
Exploring Variables(Data Anylasis)
Feature Selection
Proportion of Good vs Bad Wines
Preparing Data for Modelling
Applying different models
Choosing right model
Start doing prediciton
# ## Workflows
1. update config.yaml
2. update schema.yaml
3. update params.yaml
4. update the entity
5. update the configuration manager in src config
6. update the components
7. update the pipeline
8. update the main.py
9. update the app.py
# Learning
I learned how machine learning explainability can be used to take a deep dive into machine learning models and extract key insights that can be used in the real world. I also learned that the highest quality of wine had an alchoal content between 12% - 15%.
# License
This project is licensed under the MIT License. See the LICENSE file for details
CHEERS!
