Notes

You are working with Cardinal Vineyard who plans to release 5 new wines to celebrate their 5th year as the leading wine makers of Kentucky. They have provided you with a dataset to help them focus their efforts of preferred combinations of acidity, alcohol content, and other metrics to determine which wines should be released. Their R&D team conducted blind tasting using wines from several different companies to build this data set provided. The final dataset will include the metrics from their proposed wines, a list of 20. Cardinal Vineyard wants your help to choose their best five wines. Based on your model, they will choose the 5 highest ranking wines from the final dataset that is to be provided.
Note: this company will add more data in the future to your dataset. They are conducting a second round of wine tastings to get more data for the model.
https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009



Assignment-1
1. As a data engineer,
I want to preprocess the data handles missing values, outliers, and inconsistent data, and
performs feature engineering, (Testable task: Does the preprocessing step successfully address
missing values, outliers, and inconsistent data?)
So that the resulting dataset is clean, consistent, and enriched with informative features for
accurate analysis and modeling.
2. As a data scientist,
I want to develop a machine learning model using the dataset to predict wine quality (testable
task; Develop a machine learning model)
so that I can identify the top five wines.
3. As a marketing specialist,
I want updated list of the best wines based on the model's predictions from the data pipeline
(testable task; does the model generate a list of the top five wines?),
so that I can recommend and promote these wines to enhance sales and customer satisfaction

fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
7.4 0.7 0 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5
7.8 0.88 0 2.6 0.098 25 67 0.9968 3.2 0.68 9.8 5
7.8 0.76 0.04 2.3 0.092 15 54 0.997 3.26 0.65 9.8 5
11.2 0.28 0.56 1.9 0.075 17 60 0.998 3.16 0.58 9.8 6
7.4 0.7 0 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 5
7.4 0.66 0 1.8 0.075 13 40 0.9978 3.51 0.56 9.4 5
7.9 0.6 0.06 1.6 0.069 15 59 0.9964 3.3 0.46 9.4 5
7.3 0.65 0 1.2 0.065 15 21 0.9946 3.39 0.47 10 7
7.8 0.58 0.02 2 0.073 9 18 0.9968 3.36 0.57 9.5 7
7.5 0.5 0.36 6.1 0.071 17 102 0.9978 3.35 0.8 10.5 5
6.7 0.58 0.08 1.8 0.097 15 65 0.9959 3.28 0.54 9.2 5
7.5 0.5 0.36 6.1 0.071 17 102 0.9978 3.35 0.8 10.5 5
5.6 0.615 0 1.6 0.089 16 59 0.9943 3.58 0.52 9.9 5
7.8 0.61 0.29 1.6 0.114 9 29 0.9974 3.26 1.56 9.1 5
8.9 0.62 0.18 3.8 0.176 52 145 0.9986 3.16 0.88 9.2 5
8.9 0.62 0.19 3.9 0.17 51 148 0.9986 3.17 0.93 9.2 5
8.5 0.28 0.56 1.8 0.092 35 103 0.9969 3.3 0.75 10.5 7
8.1 0.56 0.28 1.7 0.368 16 56 0.9968 3.11 1.28 9.3 5
7.4 0.59 0.08 4.4 0.086 6 29 0.9974 3.38 0.5 9 4