This repository showcases a range of analytical models implemented directly in Microsoft Excel, demonstrating both statistical understanding and advanced spreadsheet proficiency. As a data analytics student, I created this workbook to serve as a portfolio piece illustrating my hands-on ability to analyze data, build predictive models, and present findings in a clean, structured format - all using Excel's native features.
The workbook includes the following sheets:
| Sheet Name | Description |
|---|---|
| Linear Regression | Predicts exam scores based on study hours and exam preparation. Includes manual calculation of coefficients, performance metrics, and a summary output. |
| Principal Component Analysis | Manual calculation of covariance matrix, eigenvalues and eigenvectors, and principal components for a standardized feature set. |
| Decision Tree Calculation | A rule-based decision tree example using categorical features (e.g. likes ice cream/chocolate). Uses entropy-based logic with branching conditions. |
| Logistic Model Evaluation | Detailed logistic regression model using multiple predictors. Contains odds ratios, interpretations of coefficients, predicted probabilities, and error types (Type I/II). |
| Vector Prediction Decision Tree | Manual calculation for an overfit vector prediction decision tree compared to a code generated one. |
| K-Fold Cross-Validation | Performs 5-fold cross-validation on a machine failure dataset. Calculates fold-based splits, tracks performance, and helps evaluate model generalizability. |
| Logistic Regression | Manual implementation of logistic regression on binary classification (machine working vs not). Includes logit function, probabilities, and log-likelihoods. |
| K-Nearest Neighbors | Basic setup for KNN classification. Graphical representation included. |
- Analytical Thinking: Applying core data science concepts in spreadsheet format
- Excel Mastery: Advanced use of formulas, named ranges, logical structures, formatting, and charts
- Model Interpretation: Clear presentation of each model's outputs, performance metrics, and decision logic
- Self-contained Execution: All computations are done manually within Excel — no external tools or code required
While programming languages like Python and R are industry standards for analytics, Excel remains an invaluable tool — especially in business environments. This project demonstrates how deep analytical work can be achieved even in Excel, making concepts more transparent and accessible.
- Open the
Analytical_Models_In_Excel.xlsxfile. - Navigate through the tabs to explore each model.
- Use in-sheet comments, formula breakdowns, and labeled sections to follow the logic step-by-step.
If you're a student, analyst, or recruiter reviewing this portfolio - feel free to reach out! I'm always open to feedback, collaboration, or internship opportunities in data analytics, machine learning, or related fields.
Author: Jishen Harilal
LinkedIn: www.linkedin.com/in/jishen-harilal
Contact: jishen2108@gmail.com