Data Set Information:
The two datasets are related to red and white variants of the Portuguese “Vinho Verde” wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are many more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.
Attribute Information:
Input variables (based on physicochemical tests):
- 1 – fixed acidity
- 2 – volatile acidity
- 3 – citric acid
- 4 – residual sugar
- 5 – chlorides
- 6 – free sulfur dioxide
- 7 – total sulfur dioxide
- 8 – density
- 9 – pH
- 10 – sulphates
- 11 – alcohol
Output variable (based on sensory data):
- 12 – quality (score between 0 and 10)
Description :- The dataset you’re working with is related to red and white variants of Portuguese “Vinho Verde” wine, containing physicochemical inputs such as acidity, residual sugar, chlorides, and alcohol, among others. The target variable is wine quality, rated on a scale from 0 to 10. The exploratory data analysis (EDA) focuses on understanding the distribution of wine quality and relationships between the input features. The dataset is imbalanced, making it suitable for classification or regression tasks, and SMOTE (Synthetic Minority Over-sampling Technique) is used to handle class imbalance by oversampling the minority classes.
This analysis focuses on understanding the sales trends and customer behavior during the Diwali festival season. The dataset includes details about customer demographics, product categories, purchase amounts, and city distribution.
Key Insights:
Loyalty programs and cashback offers significantly improved customer retention rates.
Top Performing Cities:
- Metro cities like Delhi, Mumbai, and Bangalore contributed the most to sales.
- Smaller cities showed a significant increase in online purchases compared to previous seasons.
Customer Demographics:
- The majority of buyers were in the 26-35 age group, indicating that young professionals are the biggest spenders during Diwali.
- Male customers accounted for a slightly higher percentage of purchases compared to females.
Product Categories:
- Electronics and Home Appliances dominated the sales, reflecting popular gifting trends.
- Fashion and Lifestyle products also showed strong performance, especially among younger customers.
Spending Patterns:
- Customers from Tier-1 cities had higher average purchase amounts compared to those from Tier-2 and Tier-3 cities.
- Discounts and festive offers played a major role in boosting overall sales figures.
Marketing Impact:
- Targeted promotions and digital campaigns led to a noticeable spike in online orders.
Conclusion:
The analysis highlights the importance of focusing on young urban professionals when planning Diwali promotions. Emphasizing electronics and lifestyle products, along with tailored discounts, can further enhance sales. Additionally, expanding outreach to Tier-2 and Tier-3 cities presents a valuable growth opportunity.