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README.md

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-# This repository contains Simplilearn's Machine Learning Projects.
+# Machine Learning Projects
+
+This repository contains machine learning project exercises, dataset references, and project descriptions from Simplilearn.
+
+## Repository Overview
+
+The repo is organized into three main sections:
+
+- `Projects/Course End Projects` — end-of-course exercises with problem statements and dataset links.
+- `Projects/Projects for Practice` — practice projects for hands-on learning.
+- `Projects/Projects for Submission` — submission-ready projects with written requirements and dataset packages.
+
+## Project Summaries
+
+### Course End Projects
+
+- `Project 9.1 - Uber Fare Prediction`
+  - Objective: Build a regression model to predict taxi fare amounts.
+  - Data includes trip details such as pickup/drop locations, passenger count, and fare.
+  - Path: `Projects/Course End Projects/Project 9.1 - Uber Fare Prediction`
+
+- `Project 9.2 - Amazon.com - Employee Access`
+  - Objective: Build a classification model to predict whether an employee should have access to a given resource.
+  - Data includes employee role and access attributes for automatic access provisioning.
+  - Path: `Projects/Course End Projects/Project 9.2 - Amazon.com - Employee Access`
+
+### Projects for Practice
+
+- `Practice Project 1 - MNIST classifier`
+  - Objective: Train a classifier on handwritten digit images.
+  - Data is stored under `Projects/Projects for Practice/Practice Project 1 -MNIST classifier/Dataset for the project/images`.
+
+- `Practice Project 2 - Phishing detector with KNN`
+  - Objective: Build a KNN-based model to detect phishing websites.
+  - Dataset includes URL and phishing indicator features.
+  - Path: `Projects/Projects for Practice/Practice Project 2 -Phishing detector with KNN/Dataset for the project`
+
+### Projects for Submission
+
+- `Project 1 - Mercedes-Benz Greener Manufacturing`
+  - Objective: Predict the time a Mercedes-Benz vehicle spends on the test bench using feature combinations.
+  - Uses a manufacturing dataset to optimize testing efficiency.
+  - Path: `Projects/Projects for Submission/Project 1 - Mercedes-Benz Greener Manufacturing`
+
+- `Project 2 - Income Qualification`
+  - Objective: Predict household income qualification levels using proxy means test features.
+  - Path: `Projects/Projects for Submission/Project 2 - Income Qualification`
+
+- `Project 3 - Phishing Detector using LR`
+  - Objective: Train a logistic regression model to classify websites as phishing or legitimate.
+  - Dataset includes 30 website parameters and a binary class label.
+  - Path: `Projects/Projects for Submission/Project 3 - Phishing Detector using LR`
+
+- `Project 4 - California Housing Price Prediction`
+  - Objective: Predict median house values for California districts using census data.
+  - Dataset includes features such as longitude, latitude, housing median age, and median income.
+  - Path: `Projects/Projects for Submission/Project 4 - California Housing Price Prediction`
+
+## How to Use This Repository
+
+1. Open the project folder you want to work on.
+2. Review the project statement and dataset links inside the project directory.
+3. Extract any ZIP archives in `Dataset for the project` folders before loading the data.
+4. Use Python `pandas`, scikit-learn, or your preferred ML framework for analysis and model training.
+
+## Notes
+
+- Some datasets are packaged as ZIP files in the submission projects and need to be extracted.
+- The `Projects` folder contains detailed problem statements and supporting documents for each exercise.