📃: Red wine Quality Detection #301
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## Pull Request for PyVerse 💡 ### Requesting to submit a pull request to the PyVerse repository. --- #### Issue Title **Please enter the title of the issue related to your pull request.** *Red wine Quality Detection* - [x] I have provided the issue title. --- #### Info about the Related Issue **What's the goal of the project?** Red wine Quality Detection by statisticall addressing and referencinf each of these features 'residual sugar', 'total sulfur dioxide', 'sulphates', 'alcohol', 'volatile acidity', 'quality' and then thoroughly taking out a decision on the overall quality. - [x] I have described the aim of the project. --- #### Name **Please mention your name.** Janvi - [x] I have provided my name. --- #### GitHub ID **Please mention your GitHub ID.** [inkerton](https://github.com/inkerton) - [x] I have provided my GitHub ID. --- #### Email ID **Please mention your email ID for further communication.** janvichoudhary116@gmail.com - [x] I have provided my email ID. --- #### Identify Yourself **Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).** GSSOC - [x] I have mentioned my participant role. --- #### Closes **Enter the issue number that will be closed through this PR.** *Closes: #issue-number* #301 - [x] I have provided the issue number. --- #### Describe the Add-ons or Changes You've Made **Give a clear description of what you have added or modified.** I have added these 7 models and evaluated their accuracy - **Logistic Regression (LR):** A linear model that predicts categorical outcomes based on input features. - **Naive Bayes (NB):** A probabilistic model based on Bayes’ theorem. - **K-Nearest Neighbors (KNN):** A non-parametric model that classifies based on the majority vote of the nearest neighbors. - **Decision Tree (DT):** A tree-based model that splits the data on feature values to predict the target variable. - **Support Vector Machine (SVM):** A model that finds the optimal hyperplane to classify data points in high-dimensional spaces. - **Linear Discriminant Analysis (LDA):** A linear model that reduces dimensionality while preserving as much class-discriminatory information as possible. - - [x] I have described my changes. --- #### Type of Change **Select the type of change:** - [ ] Bug fix (non-breaking change which fixes an issue) - [x] New feature (non-breaking change which adds functionality) - [ ] Code style update (formatting, local variables) - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) - [ ] This change requires a documentation update --- #### How Has This Been Tested? **Describe how your changes have been tested.** The project has been tested using the following methods: 1. **Cross-Validation:** - 5-fold Stratified Cross-Validation was used to evaluate the performance of each machine learning model. This ensures that the model is tested on different subsets of the data, providing a robust evaluation of its accuracy. 2. **Model Evaluation Metrics:** - Each model was evaluated using accuracy scores, which were calculated during cross-validation. This allowed comparison of the success rates of various models like Logistic Regression, K-Nearest Neighbors, Decision Tree, etc. 3. **Handling Class Imbalance:** - The dataset's imbalanced nature was addressed using SMOTE, which oversamples minority classes, ensuring that models perform well across all wine quality levels. - [x] I have described my testing process. --- #### Checklist **Please confirm the following:** - [x] My code follows the guidelines of this project. - [x] I have performed a self-review of my own code. - [x] I have commented my code, particularly wherever it was hard to understand. - [x] I have made corresponding changes to the documentation. - [x] My changes generate no new warnings. - [x] I have added things that prove my fix is effective or that my feature works. - [ ] Any dependent changes have been merged and published in downstream modules.
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🔴 Title : Red wine Quality Detection
🔴 Aim : Red wine Quality Detection by statisticall addressing and referencinf each of these features 'residual sugar', 'total sulfur dioxide', 'sulphates', 'alcohol', 'volatile acidity', 'quality' and then thoroughly taking out a decision on the overall quality.
🔴 Brief Explanation : Detecting the quality of Red Wine based on different factors like alcohol concentration and also visualizing the data for better analytical approach. And then at last rechecking the result of our model using different algorithms like LogisticRegression, GaussianNB, KNeighborsClassifier, etc. The final model is checked against 7 Algorithms to determine its success rates.
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Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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