Thanks for sharing the **WPR 1.pdf**. I’ve reviewed it and will now create a full set of **8 Weekly Progress Reports (WPRs)** in the same format and tone, following your topic:

**“Comparative Analysis of Machine Learning Models for Real Estate Price Prediction”**

These reports are designed for work that can be done **from home** and are written **in a human, academic tone**, consistent with the WPR you shared.

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## ✅ **WPR 1 (Already Submitted)**

*Assuming it included: Topic selection, tool installation, initial research.*

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## ✅ **WPR 2**

### **TARGETS SET FOR THE WEEK:**

* Study data preprocessing techniques in machine learning.
* Clean and prepare the real estate dataset for analysis.
* Understand basic evaluation metrics (MAE, RMSE, R2).

### **ACHIEVEMENTS FOR THE WEEK:**

* Completed data cleaning and removed null or incorrect values.
* Normalized and encoded necessary features from the dataset.
* Gained understanding of evaluation metrics through online tutorials.

### **FUTURE WORK PLANS:**

* Implement Linear Regression for baseline predictions.
* Visualize relationships between price and other features.
* Start preparing content for the methodology section of the paper.

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## ✅ **WPR 3**

### **TARGETS SET FOR THE WEEK:**

* Train and test Linear Regression model on the dataset.
* Record performance metrics and analyze results.
* Begin drafting the "Introduction" section of the paper.

### **ACHIEVEMENTS FOR THE WEEK:**

* Successfully trained Linear Regression and evaluated its performance.
* Observed initial results and noted issues with overfitting.
* Wrote a rough draft of the introduction with topic background.

### **FUTURE WORK PLANS:**

* Implement Decision Tree Regressor and compare with Linear Regression.
* Study how synthetic data can be generated for model training.
* Improve introduction draft based on real data insights.

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## ✅ **WPR 4**

### **TARGETS SET FOR THE WEEK:**

* Train Decision Tree and Random Forest models on the dataset.
* Compare model results using MAE, RMSE, and R2 Score.
* Study how to generate synthetic data using SDV or Faker library.

### **ACHIEVEMENTS FOR THE WEEK:**

* Implemented both Decision Tree and Random Forest algorithms.
* Compared models and found Random Forest more accurate.
* Read documentation and watched tutorials on SDV for synthetic data.

### **FUTURE WORK PLANS:**

* Generate synthetic dataset similar to real estate data.
* Train models using synthetic data and compare results.
* Begin "Literature Review" section for the research paper.

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## ✅ **WPR 5**

### **TARGETS SET FOR THE WEEK:**

* Generate synthetic data using Faker/SDV tools.
* Train models using synthetic data and note performance.
* Continue literature review based on related ML papers.

### **ACHIEVEMENTS FOR THE WEEK:**

* Generated realistic synthetic data with SDV and Faker.
* Trained models using synthetic data and recorded metrics.
* Found and summarized 4 relevant research papers for review.

### **FUTURE WORK PLANS:**

* Compare real vs synthetic data results in tabular format.
* Create graphs showing differences in model accuracy.
* Write initial draft of the "Results & Discussion" section.

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## ✅ **WPR 6**

### **TARGETS SET FOR THE WEEK:**

* Prepare visual comparison of model performances.
* Draft "Results & Discussion" section using graphs and tables.
* Analyze impact of synthetic data on model behavior.

### **ACHIEVEMENTS FOR THE WEEK:**

* Plotted graphs for R2 Score, MAE, and RMSE comparisons.
* Noted minor performance drop in synthetic data results.
* Completed 70% of the "Results" section with proper analysis.

### **FUTURE WORK PLANS:**

* Finalize "Results & Discussion" with comparison insights.
* Draft "Conclusion" and "Future Scope" sections of the paper.
* Proofread and improve earlier sections (Intro, Methodology).

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## ✅ **WPR 7**

### **TARGETS SET FOR THE WEEK:**

* Finalize all remaining sections of the research paper.
* Create references list in IEEE format.
* Start making presentation slides for project summary.

### **ACHIEVEMENTS FOR THE WEEK:**

* Completed “Conclusion” and “Future Scope” sections.
* Formatted all in-text citations and reference list.
* Prepared outline for PowerPoint presentation.

### **FUTURE WORK PLANS:**

* Design visually appealing and concise presentation slides.
* Practice 3-minute explanation for viva and presentation.
* Submit paper draft for mentor review and suggestions.

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## ✅ **WPR 8**

### **TARGETS SET FOR THE WEEK:**

* Finalize presentation and rehearse viva questions.
* Submit final version of research paper and project code.
* Complete NTCC documentation as per college format.

### **ACHIEVEMENTS FOR THE WEEK:**

* Delivered full practice presentation with time check.
* Submitted polished research paper and GitHub code link.
* Completed WPRs, certificate uploads, and final PDF report.

### **FUTURE WORK PLANS:**

* Prepare for viva with confidence on project explanation.
* Upload all required files on Amizone before deadline.
* Seek feedback from guide for future improvements.

pls rewrite this wpr on “Comparative Analysis of Machine Learning Models for Real Estate Price Prediction”. IT could be seen it is written by a human and does not include words like ieee  and viva , presentation only talk about making research by including 1.Introduction; 2. Literature Review; 3. Methodology ; 4. Results; 5. Discussion ; 6. Conclusion.