Welcome to the presentation of house price prediction today's dynamic real estate market, accurately predicting house

Welcome to the presentation of house price prediction today's dynamic real estate market, accurately predicting house prices is essential for making informed decisions whether you're buying, selling, or investing in property. Machine learning has revolutionized this process by enabling data-driven approaches that enhance prediction accuracy beyond traditional methods

1. Department of CSE(AI&ML) Academic year: 2023-24 Mini Project (21AIMP67) – Review 1 Presentation GUIDE: Dr. Lecturer Department of CSE(AI&ML) DBIT, Bengaluru PROJECT TEAM: 1. TTHANYA A J ( 1DB22CI413) 2. YESHWANTH P (1DB22CI415) 3.VIJAYALAKSHMI .J ( 1DB22CI414) 4. SIMRAN . S ( 1DB22CI412) HOUSE PRICE PREDICTION

2. 2 SL.NO. TITLE PAGE NO. 1 Introduction 03 2 Problem Statement 04 3 Objectives 05 4 Prototype Design/Model 06 5 Progress Of Work Done With Coding 07 6 Planned Code Execution 08 7 Output Discussion 09 8 Conclusion 12 9 Reference 13

3. INTRODUCTION Welcome to the presentation of house price prediction today's dynamic real estate market, accurately predicting house prices is essential for making informed decisions whether you're buying, selling, or investing in property. Machine learning has revolutionized this process by enabling data-driven approaches that enhance prediction accuracy beyond traditional methods . IN this project, we have built a machine learning model to predict the house prices of an Indian city This project will very helpful for the real estate market our model can be used by both house sellers and house buyers . Multiple Linear Regression algorithm is used to create a model with a great accuracy score 3

4. PROBLEM STATEMENT Prices of real estate properties are sophisticatedly linked with our economy Despite this, we do not have accurate measures of house prices based on the vast amount of data available. The goal of this project is to predict house prices in city based on some features such as location, size/area, number of bedrooms, and number of bathrooms. W e are using Machine Learning Algorithm to create a predictive model . In the real estate market, accurately predicting house prices is crucial for stakeholders such as buyers, sellers, and investors. Traditional methods often rely on subjective assessments or simplistic models that may not capture the full complexity of factors influencing property values. This leads to uncertainty and suboptimal decision-making in property transactions. 4

5. The objective of this project is to develop a machine learning model that can predict house prices with a high degree of accuracy based on relevant property features and market indicators. By leveraging advanced data analysis techniques, the aim is to provide stakeholders with reliable predictions that enhance their ability to make informed decisions in the dynamic real estate landscape. Enhance Detection Accuracy. Reduce False Positives. Adaptability to Evolving Tactics OBJECTIVES 5

6. PROTOTYPE DESIGN/MODEL There are sequences of important general steps involved 1. Model Objective: Train a spam detection model Algorithms: Cross-validation is a statistical method used to estimate the skill of machine learning models . 2 . Data input Objective: Prepare data for the model Import data Clean and preprocess: Remove duplicates, handle missing values, normalize text 3. Processing of data Objective: Convert data into a format suitable for the model Split data into training and testing sets 4. Prototype output Objective: Deploy and demonstrate the final ready model 6

7. Progress Of The Work Done With Coding Data Collection and Preprocessing Data Source: Collected from [specify sources, e.g., kaggle dataset] Preprocessing: Cleaned data by removing duplicates, handled missing values, and normalized text Feature Extraction Techniques Used: CountVectorizer Libraries: Scikit-learn Model Training Algorithms: Trained models using Naive Bayes, SVM, and a Neural Network Hyperparameter Tuning: Performed grid search for optimal parameters Deployment Tools: Deployed model using Flask Platform:Hosted on a local web page using Chrome for free 7

8. Planned Code Execution Data Preprocessing Objective: Prepare dataset for analysis Import data Clean: Remove duplicates, handle missing values Normalize text: Lowercase, feature scaling... Model Training Objective: Train spam detection model Split data Select algorithms: Naive Bayes, SVM Train models Tune hyperparameters Model Evaluation Objective: Evaluate model performance Metrics: Accuracy, precision, r2 -score Confusion matrix Deployment 8

9. Output Discussion Expected Outputs Spam Detection Labels: Emails/messages classified as 'spam' or 'not spam' Output Examples Example 1: Input: "Congratulations! You've won a $1,000 gift card. Click here to claim." Output: Spam Example 2: Input: "Meeting tomorrow at 10 AM. Please confirm your availability." Output: Not Spam 9

10. SOFTWARE & HARDWARE REQUIREMENTS SOFTWARE COMPONENTS: Python Html Css Jupyter Notebook Flask Pycharm Visual Studio HARDWARE COMPONENTS: Personal Computers 8GB ram 512GB/1TB hard disk 10

11. OUTCOME Improved User Experience Enhanced Security Reduced Operational Costs Filtering out 11

12. CONCLUSION & FUTURE SCOPE In conclusion , In the future, the GUI can be made more attractive and interactive. It can also be turned into any real estate sale website where sellers can give the details and house for sale and buyers can contact according to the details given on the website . To simplify it for the user, there can also be a recommending system to recommend real estate properties to the user based on the predicted price. The current dataset only includes a few locations of Bangalore city, expanding it to other cities and states of India is the future goal . To make the system even more informative and userfriendly , Google maps can also be included. . 12

13. References / Bibliography Behera, G.: Privacy preserving c4.5 using gini index pp. 1 –4 (march 2011) Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and RegressionTrees. Wadsworth and Brooks, Monterey, CA (1984) Deri, L.: nprobe: an open source netflow probe for gigabit networks. In: In Proc.of Terena TNC 2003 (2003) Fomenkov, M., Claffy, K.: Internet measurement data management challenges. In:Workshop on Research Data Lifecycle Management. Princeton, NJ (Jul 2011) Grzenda, M.: Towards the reduction of data used for the classification of networkflows. In: Proceedings of the 7th international conference on Hybrid Artificial In-telligent Systems - Volume Part II. pp. 68–77. HAIS’12, Springer-Verlag, Berlin,Heidelberg (2012), http://dx.doi.org/10.1007/978-3- 642-28931-6_7 Kim, H., Claffy, K., Fomenkov, M., Barman, D., Faloutsos, M., Lee, K.: Internettraffic classification demystified: myths, caveats, and the best practices. In: Pro-ceedings of the 2008 ACM CoNEXT Conference. pp. 11:1–11:12. CoNEXT ’08,ACM, New York, NY, USA (2008) Kobiersky, P., Korenek, J., Polcak, L.: Packet header analysis and field extractionfor multigigabit networks pp. 96 –101 (april 2009) Limwiwatkul, L., Rungsawang, A.: Distributed denial of service detection usingtcp/ip header and traffic measurement analysis 1, 605 – 610 vol.1 (oct 2004) Moore, A., Crogan, M., Moore, A.W., Mary, Q., Zuev, D., Zuev, D., Crogan, M.L.:Discriminators for use in flow-based classification. Tech. rep. (2005) Ouyang, T., Ray, S., Rabinovich, M., Allman, M.: Can network characteristicsdetect spam effectively in a stand-alone enterprise? In: Proceedings of the 12thinternational conference on Passive and active measurement. pp. 92–101. PAM’11,Springer-Verlag, Berlin, Heidelberg (2011) Schatzmann, D., Burkhart, M., Spyropoulos, T.: Flow-level characteristics of spamand ham (291) (Aug 2008) ˘Z´adn´ık, M., Michlovsk´y, Z.: Is spam visible in flow-level statistics? Tech. rep.(2009), http://www.fit.vutbr.cz/research/view_pub.php?id=9277 13