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mudasir khan  • Peerlist

Built an end-to-end pipeline using dbt, Snowflake, and AWS S3 implementing Medallion Architecture (Bronze/Silver/Gold), incremental loads, SCD Type 2, and Star Schema. https://github.com/mkyz108/airbnb-de-pipeline

Airbnb End-to-End Data Engineering Pipeline

I designed and implemented a complete post-merger sales data platform using Databricks, PySpark, SQL, Delta Lake, and AWS S3, focused on incremental processing, automation, and analytics delivery. ✅ What I Implemented 🥉 Bronze Layer (Raw Ingestion) Incremental ingestion from AWS S3 Raw data preserved for audit & replay 🥈 Silver Layer (Data Cleaning & Standardization) City name typo standardization Customer name normalization (trim, initcap) Invalid & negative price correction Duplicate record removal Schema alignment across merged datasets 🥇 Gold Layer (Business-Ready Data) Daily → monthly sales aggregation Upserts (MERGE) into unified fact tables Denormalized views for fast analytics ⏱️ Automation & Scheduling Databricks Jobs for scheduled pipeline execution Incremental loads across all layers 📊 Databricks SQL Dashboard Revenue KPIs Monthly revenue trends Top products & customers Channel-wise revenue contribution Leadership-ready single analytics layer 🛠️ Tech Stack Databricks | PySpark | Spark SQL | Delta Lake | AWS S3 | Python | Databricks Jobs | Databricks SQL Dashboards 🔗 GitHub (Full Code & Pipeline): 👉 https://lnkd.in/gAtDBafu

Built a Production-Style ETL Data Engineering

Bank XYZ has a growing customer base where the majority of them are liability customers (depositors) vs. borrowers (asset customers). The bank is interested in expanding the borrower’s base rapidly to bring in more business via loan interests. A campaign that the bank ran in the last quarter showed an average single-digit conversion rate. In the last town hall, the marketing head mentioned that digital transformation is the core strength of the business strategy, how to devise effective campaigns with better target marketing to increase the conversion ratio to double-digit with the same budget as per the last campaign. As a data scientist, I developed a machine learning model to identify potential borrowers to support focused marketing.

Digital Marketing by predicting the potential

Built an end-to-end production-style data engineering pipeline using Databricks, PySpark, SQL, Delta Lake, and AWS S3. Implemented Medallion Architecture (Bronze, Silver, Gold) with incremental data loads, automated job scheduling, and a Databricks SQL dashboard for business reporting. Key implementations: Incremental ingestion from AWS S3 Data cleaning and standardization (cities, customer names, prices, duplicates) Monthly sales aggregation and upserts using Spark SQL Automated pipeline execution using Databricks Jobs Leadership-ready Databricks SQL dashboard for revenue analytics GitHub: https://github.com/mkyz108/databricks-merger-sales-pipeline

Post-Merger Sales Data Pipeline on Databricks

I recently completed Telecom customer churn analysis project using using Logistic Regression  and I'm excited to share my insights with you all!📉📊 Every company wants to increase its revenue and profitability. To do that, while they acquire new customers, they also want to make sure that the existing ones stay with them for a long term. Also, its strategically important to know beforehand whether a set of customers are planning to stop using their services . To do that, every company or business creates and tracks customer metrics which are then used to predict their likelihood of churn. In this project, I am using the customer data of a telecom sector company based in the US to predict the probability of churn for each of the customer. I have built a model, the churn model output can also be used as a warning indicator that some customers are likely to churn. The key drivers that are making the customer more likely to churn can be alleviated and ensure that the customers are actually retained. I have performed following ➡️ Perform basic EDA to get familiarize with the data ➡️Create new features that might add value to the model ➡️Define a function for each set of code that might need to be repeated again ➡️Prepare the data for modelling ➡️Create models using multiple algorithms ➡️Perform Hyper-parameter tuning to get the best parameters ➡️Compare the performance of different models using multiple metrics ➡️Apply feature reduction methods to get a light model while maintaining performance ➡️Check whether the top model features are as per your business understanding/intuition ➡️Save the model and creating API to implement the model in production #DataScience #MachineLearning #ChurnPrediction #Telecom #LogisticRegression #PredictiveAnalytics #DataAnalysis #DataDrivenInsights #Rogers #Bell #Telus #CustomerRetention #DataVisualization #PredictiveModeling #AI #BigData

Predict Churn for a Telecom company using Log

The past decade has seen exponential growth in the telecommunication industry. The cost of communication has steadily decreased, and thanks to innovation in the electronics industry, mobile phones have become affordable and feature-rich. Telecommunications is one of the most data-intensive industries in the world, generating vast amounts of data on customers, network performance, operations, and more. In a hyper-competitive industry, telecommunication companies must differentiate their offerings and target customers effectively to be profitable and successful. With the information collected about customer behavior and preferences, data in telecom means bigger opportunities for telecommunication companies. Using data science in telecom, telecommunication companies are combining various real time insights like interests, location, activities with the habits and preferences to achieve unparalleled marketing power.