customer personality analysisjjjjjjjjjjj.pptx

hhgggggjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn,,,,,,,,,,,,,,,,,,,,,,,,,,,,uiiiiiiiiiiihihhhhhhhhnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn

1. Customer personality analysis Group members : Tanu     Rupa Diksha   Milind Spoorthi       Supriya Mentor Name: Bapuram Pallavi

2. Objectives : Customer personality analysis helps a business to modify its product based on its target customers from different types of customer segments. For example, instead of spending money to market a new product to every customer in the company’s database, a company can analyse which customer segment is most likely to buy the product and then market the product only on that particular segment. 2

3. Steps involved : 3

4. EDA Exploratory Data Analysis (EDA) is an approach that is used to analyse the data and discover patterns, or check assumptions in data with the help of statistical summaries and graphical representations;   It can also help determine if the statistical techniques you are considering for data analysis are appropriate. 4

5. Model details Clearly define the goals and objectives of the customer personality analysis. Explain what insights the project aims Data Collection and Sources : Detail the types of data collected (e.g., demographic, behavioral, transactional) and the sources from which this data was obtained Model Selection and Rationale : Describe the models and algorithms used for the analysis (e.g., clustering, classification, regression). Feature Engineering : Discuss the process of feature engineering, including the selection of relevant features and any transformations or aggregations performed on the data. Model Training and Evaluation : Explain the process of training the models, including any cross-validation techniques used. Present the metrics used to evaluate model performance Outline the steps for implementing the insights into business processes. Discuss any potential challenges and how they can be addressed. Highlight future enhancements or additional analyses

6. Heat map :

7. Difference in Marital Status Married 864 Together 580 Single 480 Divorced 232 Widow 77 Alone 3 YOLO 2 Absurd 2 7

8. 8 Bi-variate analysis

9. Clustering : Clustering is used to identify groups of similar objects in datasets with two or more variable quantities. Clustering methods, such as Hierarchical, Partitioning, Density-based, Model-based, and Grid-based models, assist in grouping data points into clusters. These techniques use various methods to determine the appropriate result for the problem.  Clusters are loosely defined as groups of data objects that are more similar to other objects in their cluster than they are to data objects in other clusters .

10. K-MEANS 20XX Pitch Deck 10 Elbow curve / Scree plot kmeans = KMeans ( n_clusters = 3)

11. Getting centroid for Agglomerative Clustering 11

12. After the model validation based on the accuracy scores compared to others the gradient boosting is the better model and so is used for the following model deployment. 12

13. challenges faced First, data collection is a critical hurdle; gathering comprehensive and accurate data from multiple sources while ensuring privacy and compliance with data protection regulations is complex and resource-intensive. Second, data integration and preprocessing present difficulties as diverse data types and formats need to be harmonized to ensure consistency and reliability. Third, model selection and validation are crucial, requiring the identification and fine-tuning of algorithms that can accurately capture and predict personality traits from behavioral data. Additionally, there is the challenge of interpretability; ensuring that the models' outputs are understandable and actionable for business stakeholders often necessitates a trade-off between model complexity and transparency. Moreover, scalability poses a significant issue, as the system must efficiently process increasing volumes of data without compromising performance.  Finally, ethical considerations must be addressed, including ensuring fairness and avoiding biases in the analysis to maintain customer trust and adhere to ethical standards. 15

14. THANK YOU 14