Findata Analysis Boost Business

Using customer churn data, you will employ statistical techniques such as linear regression and decision trees to uncover critical patterns and trends. Data visualization methods like heatmaps and bar charts will be utilized to showcase your findings. Machine learning algorithms, including random forest and k-means clustering, will help enhance predictive capabilities. To analyze the data, you will use Python and Jupyter Notebook, along with popular libraries like Pandas, NumPy, Matplotlib, and Scikit-learn. Data preprocessing steps will involve handling missing values and encoding categorical variables. You may also employ advanced machine learning techniques such as gradient boosting and time series analysis for further insights. Collaborate with marketing and product development teams to ensure your analysis addresses the organization's needs and adds value. Present your findings through an interactive dashboard and slide presentation, making complex information accessible and engaging for your audience. By doing so, you will empower stakeholders with data-driven insights that inform strategic decisions and drive success.