Project Overview:

As the lead data analyst for a large-scale e-commerce website involving millions of users and products, I was primarily responsible for data cleansing, preprocessing, and analysing user behaviours.

Data Cleaning and Preprocessing:

In the initial stages, I found that the raw data presented numerous issues such as missing values, outliers, and inconsistent data formats. To rectify this, I leveraged Python for data preprocessing and cleansing. For instance, I utilized the Pandas library to fill in missing values, the NumPy library for detecting and filtering outliers, and Regular Expressions to standardize data formats.

Model Building and Prediction:

Upon cleaning and preprocessing the data, I used Scikit-Learn to construct multiple models, including Decision Trees, Random Forests, and K-Nearest Neighbors to predict users' purchasing behaviours. During the model selection phase, I implemented cross-validation for each model to choose the most optimal one.

Insights and Recommendations:

Post model construction, I derived insights into user purchasing behaviour and preferences, identifying key patterns such as when users were most likely to make a purchase, and what types of products appealed to them the most. Consequently, based on these profound analyses, I put forth recommendations for optimization of the clients' recommendation system. This included suggestions such as recommending products that customers might be interested in based on their purchase history, and suggesting items they might browse based on their browsing history.

Outcomes:

Post optimization, the website experienced a substantial 15% increase in sales, testifying the efficacy of our analysis.

Conclusion：

The project was not only successful in enhancing the sales for the e-commerce website but also provided deeper insights into user behaviour and preferences, consequently informing business strategy.