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From Raw Logistics Data to Business Insights: An Excel Analytics Project (RFM Analysis)

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Analyzed logistics shipment data in Excel to evaluate delivery performance, cost, transit time, distance trends. Extended the analysis with RFM-based carrier segmentation to understand carrier utilization and spending patterns, supporting more data-driven operational decisions. My objective was to explore two practical business questions: How are delivery delays and shipment outcomes behaving across time and distance? How can carrier performance be evaluated using cost, transit time, shipment activity, and delivery performance? Starting with data quality I first reviewed the dataset for completeness and consistency. The initial cleaning process included identifying missing delivery dates and transportation costs, checking transit-day inconsistencies, and investigating numerical outliers. One thing I wanted to avoid was treating every unusual value as bad data. After null-value treatment, the working dataset contained 1,928 shipment records , while the dataset used after the selected ...
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How I Analyse Database Table Relationships Before Starting Analysis

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When I am introduced to a new project, one of my first priorities is  understanding the D ata Landscape, means how the Data is spread across. The sources are mostly from Excel and SQL databases for the structured data.  Before understanding the KPIs, dashboard behaviour, I'll try to understand below things What tables exist? (Must explain List of Tables and Overview of each) What does each table represent? ( Must explain in detail about each table) Which columns uniquely identify a record? (Keys present in the table or not to establish relations) Are relationships already defined? (To see if there's connectivity established or not) Note: There’s still a lot more to explore and analyze, but for this post, we’ll stop here. This is only the beginning — there’s much more analysis to come.  Here is the approach I follow for a database. 1. Start with Data Discovery — Understand What Exists My first step is going into the appropriate correct shcema/database and creating an inve...
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Understanding Titanic Survival Predictions with Decision Trees

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  Decision Tree: Supervised learning Algorithm Can do both Regression and Classification capabilities Inverted tree like structure with bunch of if-else statements The Titanic dataset is one of the most famous case studies in data science. It captures passenger details such as age, sex, class, fare, and family aboard, along with whether they survived or not. In this post, we’ll walk through how a decision tree model uses these features to predict survival, and what the visualization reveals. How the Decision Tree Works Note: These features are based on the importance table at the right side Root node: “Was the passenger male?” If female , survival chances are much higher. The tree then considers passenger class and fare to refine predictions. If male , survival chances drop significantly, but age and family aboard (siblings/spouses) still play a role. Each node in the tree shows: Gini impurity: How mixed the group is (lower / purer). Samples: Number of passengers in that group. ...
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Jupyter Lab Configurations

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To list all installed kernels in your JupyterLab system, open a terminal or command prompt and run bash jupyter kernelspec list ----------------------------------------------------------------------------------------------------------------------------- Creating new Kernel for specific purpose 1. Create and Activate a Virtual Environment bash python -m venv .venv source .venv/bin/activate # On macOS/Linux .venv \ Scripts \ activate # On Windows 2. Install IPython Kernel in the Environment bash pip install ipykernel 3. Register the Kernel bash python -m ipykernel install --user --name myenv --display-name "Python (myenv)" --name is the internal name used by Jupyter. --display-name is what you'll see in JupyterLab's kernel list. 4. Remove: jupyter kernelspec uninstall myenv ----------------------------------------------------------------------------------------------------------------------------- Changing default settings of Linewrap and LineNum...
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Databricks Fundamentals

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+ Databricks is an integrated platform for data and AI that enables large-scale development, deployment, and management of data, analytics, and AI-driven solutions. + Founded in 2013 at  University of California, Berkeley + Mission: democratize data and AI + 2020- Introduced Lakehouse + 2023- Rise of GenAI (Data +AI)  Some Basic Terms: Data Warehouse:  Storage of Structured data for BI & reporting Data Lakes:  Storage of structured, semi & unstructured data used for almost all type of data suitable                               for DS,ML we can't use both by connecting each other i.e; as both storage systems with same data to be ingested. People are copying the data to use for each application as for each  system the data governance and security rules are different Data Lakehouse:  (Data Warehouse + Data Lake) one platform to unify all data, analytics and AI wo...
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