Event Name: Workshop on PREDICTIVE ANALYTICS using SPSS	Event Date: 10th Oct 2023
Faculty Coordinators: Ms. Neha Issar, Assistant Professor	Event Timings: 1 pm-4 pm
Number of Participants:42	Venue:IBM lab

OBJECTIVE: To identify the vulnerabilities earlier so that they can find out which are the risks that are acceptable and which are not.

Detailed Report:

On 2023-10-10, Lloyd Business School, Greater Noida organized a workshop about PREDICTIVE ANALYTICS using SPSS.

SPSS (Statistical Package for the Social Sciences) is a software program used by researchers in various disciplines for quantitative analysis of complex data

Predictive analytics is a branch of advanced analytics that uses historical data to predict future outcomes. It uses statistical modeling, data mining techniques, and machine learning to identify patterns and trends in data. Predictive analytics can be used in a wide range of industries, including healthcare, finance, marketing, and sales.

SPSS is a statistical software package that can be used for predictive analytics. It offers a variety of tools and algorithms for data preparation, model building, and model evaluation. SPSS is also easy to use, making it a good choice for both beginners and experienced users.

There are two SPSS types:

• Variable View.
• Data View.

SPSS in data analytics can be used as to perform data entry and analysis and to create tables and graphs. SPSS is capable of handling large amounts of data and can perform all of the analyses covered in the text and much more. Just like-

• Data preparation: This involves cleaning and transforming the data so that it is ready for analysis. This may include removing outliers, filling in missing values, and converting categorical variables to numerical variables.
• Model building: This involves choosing a predictive model and training it on the data. SPSS offers a variety of predictive models, including logistic regression, decision trees, and random forests.
• Model evaluation: This involves assessing the performance of the predictive model on a held-out test set. This will help to ensure that the model will generalize well to new data.

Predictive Analytics can be used across the board in any industry, provides greater insights on how to find your best customers and how to predict behaviors and preferences so you can reduce customer turnover, prevent fraud, improve processes or grow revenue.

SPSS offers a variety of predictive models, such as logistic regression, decision trees, and random forests. It is a good idea to try different models to see which one performs best on your data. Cross-validation is a technique for evaluating the performance of a predictive model on a held-out test set. It is a good idea to use cross-validation when evaluating your predictive models. Feature engineering is the process of creating new features from existing features. This can help to improve the performance of your predictive models. It is important to interpret the results of your predictive models. This will help you to understand the factors that are most important for predicting the outcome of interest.

An Example of how SPSS Actually Works.

Suppose we want to predict whether a customer will cancel their subscription or not. We have a dataset of historical customer data, including variables such as customer demographics, usage patterns, and support tickets.

To build a predictive model in SPSS, we would first need to prepare the data. This would involve cleaning and transforming the data so that it is ready for analysis. For example, we would need to remove outliers, fill in missing values, and convert categorical variables to numerical variables.

Once the data is prepared, we can start building the predictive model. We can use SPSS's built-in predictive modeling algorithms, such as logistic regression or decision trees. Once the model is built, we need to evaluate its performance on a held-out test set. This will help to ensure that the model will generalize well to new data. If the model performs well on the test set, then we can deploy it to production.

Predictive analytics can be a powerful tool for businesses. It can help businesses to make better decisions, improve efficiency, and increase profits. SPSS is a good choice for predictive analytics because it offers a variety of tools and algorithms for data preparation, model building, and model evaluation.

Overall, the workshop session was a success. The participants learned about tool IBM SPSS Modeler, and its importance in present times and coming future especially with regards to Business Analytics.