Predictive Business Analysis Lab

Course Objectives:

1. To equip students with the necessary knowledge and skills to perform predictive analysis on business-related datasets.
2. To enable students to apply machine learning algorithms and tools effectively for solving business prediction tasks.

Course Outcomes:

• CO1 Gain practical expertise in applying predictive analysis techniques to solve real-world business problems.
• CO2 Develop the ability to analyze and interpret business data, make data-driven decisions, and present actionable insights.

List of Experiments:

1. Write a program to analysis of a business dataset to identify patterns, correlations, and trends. Visualize the data using techniques such as histograms, scatter plots, and box plots to gain insights into the data distribution and relationships between variables.
2. Build a regression model to predict a business metric, such as sales or customer churn, based on relevant features.
3. Write a program to apply different regression algorithms, such as linear regression, decision trees, or random forests, and compare their performance using evaluation metrics like Mean Squared Error (MSE) or R-squared.
4. Perform a classification analysis to predict a binary or multi-class business outcome, such as customer segmentation or product categorization. Experiment with classification algorithms like logistic regression, support vector machines (SVM), or decision trees. Evaluate the models using metrics such as accuracy, precision, recall, and F1-score.
5. Write a program to apply time series forecasting techniques to predict future business trends or demand for a product or service.
6. Write a program to Investigate the impact of feature selection and engineering techniques on predictive modeling performance.
7. Write a program to experiment with different methods such as correlation analysis, stepwise regression, or Recursive Feature Elimination (RFE) to identify the most relevant features. Compare the performance of models with and without feature engineering. 
8. Write a program to explore the effectiveness of ensemble methods, such as bagging, boosting, or stacking, in improving predictive performance.
9. Write a program to Analyze textual data, such as customer reviews or social media comments, to extract sentiment and gain insights into customer opinions. Apply techniques like Natural Language Processing (NLP), text preprocessing, and sentiment analysis algorithms to classify text into positive, negative, or neutral sentiments.
10. Write a program to analyze the resulting customer segments based on demographic, behavioral, or transactional characteristics to identify distinct customer groups and target them with tailored marketing strategies.
11. Write a program to design an A/B test to evaluate the impact of a business intervention or change on key metrics.
12. Simulate different business scenarios, such as pricing strategies, inventory optimization, or resource allocation, using predictive models.