A real estate valuation tool that predicts residential property prices in Bengaluru using Linear Regression trained on 13,320 actual property listings. Extensive data cleaning and domain-knowledge outlier removal improves model R² from 0.63 to 0.845.
Tech Stack: Python, Linear Regression, Scikit-learn, Flask, Pandas, Feature Engineering, Real EstateDownloads: 121
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Customizable — Make It Your Own
The report, PPT slides, and source code are fully editable. Change the title page,
add your college name, modify sections, rename variables, extend features — anything your college or
guide requires. No restrictions whatsoever. The pre-trained model
is bundled inside — no training from scratch required.
👀 See What's Inside
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Academic Project Report
Professionally formatted in IEEE/University standard format. Approx 40 pages.
Presentation Slides
Clean, modern, and ready for your final year presentation. Approx 12 to 15 slides.
Internship Certificate
Official 4/8/12 week internship completion certificate.
Project Certificate
Unique QR-coded project completion certificate.
Viva Questions Prep
Detailed technical Viva Q&As with explanations.
Architecture Diagram
System workflow flowchart & data processing pipeline specs.
LinkedIn Post Draft
Ready-to-post LinkedIn content to showcase your project.
Prepare for your project viva with these likely questions and suggested answers.
1. What is the core objective of the House Price Prediction System and what real-world problem does it solve?▼
2. Explain the system architecture and end-to-end data flow in House Price Prediction System.▼
3. Why was Python, Linear Regression, Scikit-learn, Flask, Pandas, Feature Engineering, Real Estate selected for this project over alternative frameworks?▼
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Viva
Questions Locked
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questions unlock after purchase.
Copy-ready resume bullet points are included with this project.
• Engineered and deployed the House Price Prediction System, providing a robust solution for machine learning / ai challenges.
• Utilized a modern technology stack including Python, Linear Regression, Scikit-learn, Flask, Pandas, Feature Engineering, Real Estate to build a highly scalable, secure, and modular application architecture.
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Resume
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