wine-variety

Contents of the Project:-

1. Model used
2. Features extracted
3. Model accuracy in train
4. Data Visualization
5. Top 5 actionable insights
6. Saving the Output in a csv
7. Licensing

Elaboration of the contents :-

1) Model used : SVM and Decision Tree (Classification)

2) Features extracted:(price, points, provinces)

3) Model Accuracy in Train:(accuracy_score =0.72216,f1_score = 0.6627)

4) Data Visualization(Decision Tree):

5) Top 5 actionable Insights from the Data:

a) We can determine the "Significant Factors" or "Dominating Factors" that affects the prediction and thus in a way the mindset of the customer groups.

b) Using this Classication groups, we could easily "group" or "cluster" similar customer together for better implications and operations.

c) By the visualization of data using Decision Tree , we could easily see the "Randomness" or "dissimilities bewteen customers in the same group and other groups as well.

d) We can easily determine the "popularity" of the variety among the customers and the reason for it as well.

e) The most crucial insight is "Increasing the net profit (prices)" depending on the popularity determined earlier and make tactics from buisness point of view.

6) Saving the Output in a csv:

After predicting is saved automatically in the test.csv file under the column name "Predicted Value":

Before running the script:

After running the script:

7)Conversion of variety to its unique counterpart:

License

The project is available as open source under the terms of the MIT License.