Market Segmentation Analysis for E-commerce Company

Using Python's powerful data manipulation library, Pandas, I loaded and preprocessed the e-commerce data. This involved handling missing values, data cleaning, and transforming the dataset into a suitable format for analysis. With Pandas, I organized the data into structured tables, allowing for efficient exploration and analysis.

Next, I leveraged NumPy for advanced numerical computations. This library provided essential functionalities for calculating metrics such as customer lifetime value, average order value, and revenue. NumPy's efficient array operations allowed for speedy calculations, enabling me to extract valuable insights from the data.

To gain deeper insights and visualize patterns, I utilized data visualization libraries such as Matplotlib and Seaborn. These libraries enabled me to create various types of charts, graphs, and plots, illustrating key performance indicators, sales trends, customer segments, and product performance. Visualizations helped in identifying patterns, correlations, and outliers, enabling the company to make data-driven decisions.

Furthermore, I employed machine learning techniques using scikit-learn to predict customer churn. By building predictive models and utilizing algorithms such as logistic regression, decision trees, or random forests, I identified potential churn indicators and developed a customer churn prediction system. This empowered the company to proactively implement retention strategies, reducing customer attrition rates.

Overall, the Python programming language, along with the data analysis libraries, played a crucial role in enabling the e-commerce company to make insightful decisions. By leveraging Python, Pandas, NumPy, scikit-learn, Seaborn, and Matplotlib, the company gained valuable insights into customer behavior, sales patterns, and churn prediction, ultimately leading to improved customer retention and business growth.