https://github.com/hiteshydv001/wine-price-predict
wine-quality-predict
https://github.com/hiteshydv001/wine-price-predict
Last synced: about 1 month ago
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wine-quality-predict
- Host: GitHub
- URL: https://github.com/hiteshydv001/wine-price-predict
- Owner: Hiteshydv001
- Created: 2023-09-24T05:31:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-27T14:29:55.000Z (over 1 year ago)
- Last Synced: 2025-02-13T03:44:22.533Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 565 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Wine-Quality-Prediction
In this project, we have developed a machine learning model to predict the quality of wines. Wine quality is an important factor for both producers and consumers. By accurately predicting wine quality, producers can improve their production processes, and consumers can make more informed choices.
## Dataset Used
**Dataset from kaggle:**
We used the Wine Quality Dataset for training and evaluating the model. This dataset contains various chemical properties of wines, such as acidity, pH, alcohol content, and quality ratings.
- You can find the dataset here: https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009
## Roadmap
**Data Collection and Preprocessing:**
- Gather a dataset containing information about wines, including features like acidity, pH, alcohol content, and quality ratings. Make sure the data is clean and well-structured. Split the dataset into features (input variables) and labels (quality ratings). Perform data preprocessing, which may involve handling missing values, scaling features, and encoding categorical variables.
**Exploratory Data Analysis (EDA):**
- Conduct EDA to understand the distribution of data, identify outliers, and visualize relationships between features and wine quality. EDA helps you gain insights into the dataset and informs feature selection or engineering decisions.
**Feature Engineering:**
Create new features or transform existing ones to improve the predictive power of your model. Feature engineering can involve statistical calculations, interaction terms, or domain-specific knowledge.
**Data Splitting:**
- Split the dataset into training, validation, and test sets. A common split is 70% for training, 15% for validation, and 15% for testing. This allows you to train, tune, and evaluate your model separately.
**Select a Machine Learning Algorithm:**
- Choose a suitable machine learning algorithm for regression tasks. For wine quality prediction, regression algorithms like Linear Regression, Decision Trees, Random Forest, Support Vector Regression, or Gradient Boosting can be good options.
**Model Training:**
- Train your chosen machine learning model on the training dataset. The model learns to make predictions based on the input features. Experiment with hyperparameter tuning to optimize the model's performance. Techniques like cross-validation can help you select the best hyperparameters.
**Model Evaluation:**
- Evaluate the model's performance on the validation dataset using appropriate regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) score. Visualize the model's predictions against actual wine quality ratings to assess its accuracy.
## Screenshots
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## Tech Stack
**Languages:** Python
**Framework:** Jupyter Notebook || Pycharm
## 🔗 Links
## let's connect
[](https://www.linkedin.com/in/hitesh-kumar-4b2735252/)