Learning Path

🚀 Customer Analytics

I recently completed a Kaggle project where I explored how to turn raw customer data into meaningful, actionable insights. The goal was to uncover patterns and trends that could drive better decision-making for businesses.

🔍 Project Process:

• Data Exploration: I started with an exploratory analysis to understand the structure, detect missing values and identify initial patterns.
• Cleaning and Preprocessing: I cleaned the data, handled null values and elimination of unused variables.
• Analysis and Visualization: I used tools such as Pandas, Plotly and scikit-learn to visualize the key segmentations, as well as visually identify the optimal number of clusters to segment the database.
• Modeling & Insights: Applied clustering algorithms (e.g., K-Means) to segment customers into distinct groups. Identified high-value customers and recommended targeted marketing strategies.
• Results Interpretation: I extracted actionable insights, such as identifying high-value customers and recommending personalized strategies.

📊 What I Learned.

• The importance of good data cleansing to ensure reliable results.
• How effective visualizations can communicate complex insights in a simple way.
• The power of machine learning to analyze behaviors and make data-driven decisions.

🔗 About

Check out the full project here: Transforming Customer Data into Actionable Insights