S4-B13-WINE QUALITY PREDICTION

[22h51a7386]

This is G.Anand from team 13 ,section 4.

Project Name: Wine Quality Analysis and Prediction WhatsApp Unknown 2024-06-27 at 10.10.10 AM.zip

Objective:- To assess and predict the quality of wine based on its chemical properties, providing insights for wine producers and connoisseurs to improve and evaluate wine quality.

This project leverages data science and machine learning techniques to analyze a comprehensive dataset of wine characteristics, aiming to accurately predict wine quality. The dataset includes critical features such as fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, and sulphates.

Libraries Used:-

->Pandas and NumPy for data manipulation and numerical operations, enabling efficient data cleaning and transformation.

->Matplotlib and Seaborn for creating insightful visualizations that uncover trends and relationships within the data.

->Scikit-learn for building and evaluating robust machine learning models, crucial for making accurate quality predictions.

->Statsmodels for conducting detailed statistical analysis, offering deeper insights into the significance of various features.

The project begins with loading the wine quality dataset and performing exploratory data analysis to understand the distribution and relationships of different chemical properties. Visualizations such as histograms and pair plots help in identifying patterns and potential outliers.

Next, data cleaning ensures the dataset is free of missing or invalid values, followed by feature engineering to create new, informative features. Machine learning models, including Random Forest and Linear Regression, are then trained on the data to predict wine quality. These models are evaluated using metrics like accuracy and precision, ensuring they provide reliable predictions.

Statistical analysis is conducted to identify the most significant features influencing wine quality. This comprehensive approach not only predicts wine quality with high accuracy but also provides valuable insights into which chemical properties are most critical.

Use of the Project:-

->Wine producers can refine their processes by focusing on key chemical properties. ->Wine connoisseurs and consumers can benefit from a reliable quality assessment tool. ->Researchers gain a deeper understanding of the factors affecting wine quality, aiding further studies in oenology.

This project showcases the power of data science in the field of oenology, offering a blend of predictive modeling and statistical analysis to enhance the understanding and production of high-quality wine.

Steps and Outputs:-

Data Loading:- The dataset winequality.csv is loaded using Pandas. Output: Initial few rows of the dataset. Summary statistics of the dataset.

Data Exploration:- Exploratory Data Analysis (EDA) is performed to understand the structure and summary of the dataset. Output: Information about the dataset (data types, non-null counts). Statistical summary of features. Distribution plots of the quality feature.

Data Cleaning:- Data cleaning steps include handling missing values and removing invalid data points. Output: Cleaned dataset without missing values. Summary of changes made during the cleaning process.

Data Visualization:- Visualizations are created to understand the distribution of each feature and their relationship with the target variable (wine quality). Output: Histograms and KDE plots of each feature. Pair plots to show relationships between features and the target variable. Correlation heatmap.

Feature Engineering:- New features are created, or existing features are transformed to improve model performance. Output: Dataset with new/engineered features. Description of the new features created.

Model Building:- Machine learning models are built to predict wine quality. Models include Random Forest, Linear Regression, etc. Output: Splitting of data into training and testing sets. Trained machine learning models. Predictions on the test set.

Model Evaluation:- Models are evaluated using metrics like accuracy, precision, and recall. Output: Accuracy score of each model. Classification report including precision, recall, and F1-score. Confusion matrix.

Statistical Analysis:- Statistical analysis is conducted to understand the significance of different features. Output: Summary of the statistical model. p-values and coefficients of features.

these are the following picturs of the output:-

[22h51a7386]

You said to resend pr that's why I send it

[22h51a7386]

Is it fine to merge my pr

[22h51a7386]

Please give reply