Update README.md

Author: jishen-harilal

README.md

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-# analytical-models-in-excel
-A curated Excel workbook showcasing core data analysis techniques - including regression, classification, dimensionality reduction, and cross-validation - implemented entirely within spreadsheets. Ideal for demonstrating manual model logic, clean formatting, and advanced Excel proficiency without code.
+# 📊 Analytical Models in Excel
+
+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.
+
+---
+
+## 📁 Workbook Overview
+
+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.                                                                                                     |
+
+---
+
+## 🔧 Skills Demonstrated
+
+* **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
+
+---
+
+## 🧠 Why Excel?
+
+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.
+
+---
+
+## 🚀 How to Use
+
+1. Open the `Analytical_Models_In_Excel.xlsx` file.
+2. Navigate through the tabs to explore each model.
+3. Use in-sheet comments, formula breakdowns, and labeled sections to follow the logic step-by-step.
+
+---
+
+## 📬 Feedback & Collaboration
+
+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