House Price Prediction Prediction Prediction (1).pptx

House Price Prediction Prediction Prediction (1).pptx

1. DS & ML Group Project House Price Prediction

2. ML Section Summary Trained 3 Machine Learning Models Linear Regression Ridge Regression Random Forest Regressor Selected Best Model Random Forest chosen based on performance metrics (lowest RMSE, highest R²) Visualized Results Created and saved key plots: residuals, actual vs. predicted Prepared for Deployment Model saved as ' trained_model.pkl ' and integrated with Streamlit app

3. SECTION 1 : INTRODUCTION

4. Project Title : House Price Prediction Tools: Python, Streamlit , Scikit-learn, Pandas, Seaborn Dataset : House Prices - Advanced Regression Techniques

5. Objective of the Project Predict House Prices Using Machine Learning Apply regression algorithms to estimate property prices based on key features like location, area, number of rooms, etc. Develop a User-Friendly Web Application Create an interactive interface where users can input house details and receive instant price predictions. Visualize Key Insights Display informative graphs and charts to help users understand the trends and factors influencing house prices. Deploy a Predictive Model Integrate and host the trained ML model to provide real-time predictions via the web app.

6. Problem Statement Real Estate Pricing is Complex Property prices vary widely based on location, size, amenities, and market conditions. Multiple Interdependent Features Price is influenced by a combination of factors that are often interrelated and non-obvious. Limitations of Traditional Methods Conventional pricing models struggle to handle non-linear patterns and large feature sets. Need for Smarter Prediction Machine Learning can capture complex relationships between variables, leading to more accurate and data-driven predictions

7. Motivation Empower Buyers and Sellers Provide accurate, instant property value estimates to support better financial decisions. Support Realtors and Property Evaluators Assist professionals with reliable, data-backed pricing insights to enhance trust and transparency. Automate the Pricing Process Replace guesswork with a machine learning model that offers fast, consistent, and objective price predictions.

8. Scope of the Project Data Preprocessing & Feature Selection Clean and prepare the dataset, selecting the most relevant features for accurate prediction. Model Training & Evaluation Use regression algorithms (like Linear Regression, Random Forest, etc.) to train and test the predictive model. Data Visualization Generate graphs to explore the relationship between price and key factors such as area, location, and number of rooms. Web Application Development Build an interactive web app using Streamlit that allows users to input house details and get real-time price predictions.

9. Methodology Overview 1. Data Cleaning Handle missing values, remove duplicates, and standardize data formats to ensure high-quality input. 2. Feature Engineering Select and transform relevant features (e.g., area, location, BHK) to improve model performance . 3. Model Selection Apply and compare various regression models (e.g., Linear Regression, Decision Tree, Random Forest) to find the best performer. 4. Evaluation Use metrics like R² Score, MAE, RMSE to evaluate the accuracy and reliability of the model. 5. Deployment Integrate the best-performing model into a Streamlit web application for real-time user interaction and predictions.

10. Tools and Libraries Used Python – Core programming language for the entire project. pandas & numpy – Efficient data manipulation and numerical operations. matplotlib & seaborn – Visualizing data trends and feature relationships. scikit-learn – Implementing machine learning models and evaluating performance. joblib – Saving and loading trained ML models efficiently. Streamlit – Building an interactive and user-friendly web interface for real-time predictions.

11. System Architecture

12. ML Pipeline Diagram

13. Real-World Scenario: House Price Estimation Made Easy A user wants to estimate the price of a house based on its specifications like area, location, number of rooms, etc. The user inputs these values in the Streamlit sidebar (e.g., dropdowns, sliders, text fields). The app instantly displays the predicted price , along with supporting graphs that visualize how different features influence the price.

14. Section 2 : DATASET ANALYSIS

15. Dataset Description train.csv  - the training set test.csv  - the test set data_description.txt  - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here sample_submission.csv  - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms

16. Selected Features Features chosen based on correlation with the target variable ( SalePrice ) and practical relevance : OverallQual – Overall material and finish quality GrLivArea – Above ground living area (sq ft) GarageCars – Number of cars the garage can hold GarageArea – Size of the garage (sq ft) TotalBsmtSF – Total basement area (sq ft ) FullBath – Number of full bathrooms YearBuilt – Year the house was built

17. Handling Missing Values Dropped Rows Removed records containing NA (null) values in the selected key features to maintain data quality. Data Integrity Ensured the final dataset was clean and consistent , avoiding errors during model training and prediction. Focus on Simplicity Chose to drop rather than impute missing values to keep the pipeline straightforward and reliable

18. Correlation Heatmap

19. GrLivArea vs Sale Price Graph

20. Garage Area vs Sale Price Graph

21. TotalBsmtSF vs Sale Price Graph

22. Year Built vs Sale Price Graph

23. Summary Statistics Mean : Gives the average house price Median : Shows the central tendency, less affected by outliers Standard Deviation (Std) : Measures the spread/variability of prices Descriptive Stats for Numeric Columns : Used .describe() to summarize key numerical features such as: GrLivArea (Living Area) TotalBsmtSF (Basement Size) GarageArea , YearBuilt , etc. Includes: count, mean, min, max, 25%, 50%, 75% Purpose : Helps understand feature scales, detect outliers, and shape model preprocessing .

24. SECTION 3: MACHINE LEARNING

25. Features Selection Selected 7 Key Features Chosen based on strong correlation with SalePrice , domain significance, and data availability. Benefits : Reduced dimensionality , making the model simpler and faster Lower risk of overfitting Improved interpretability of results

26. Train-Test Split Splitting Strategy : 80% of the data for training 20% for validation/testing Tool Used : Utilized train_test_split () from scikit-learn Set random_state =42 for reproducibility of results Purpose : To train the model on one portion and evaluate its performance on unseen data.

27. Algorithm Used A Linear Regression A simple and interpretable baseline model to predict continuous values. Ridge Regression Regularized version of linear regression to reduce overfitting and handle multicollinearity. Random Forest Regressor An ensemble-based model that improves accuracy by combining multiple decision trees. Implementation All models were implemented using the scikit-learn library ( sklearn ). Purpose Compared performance to select the best model for deployment based on accuracy and generalization.

28. Python Libraries (Imports)

29. Model Training Loop Iterated Over Multiple Models Trained and compared models like Linear Regression, Ridge, and Random Forest. Training Process Fit each model on X_train Predict on X_val Evaluation Calculated metrics like R² Score , MAE , and RMSE Stored results for comparison Purpose Identify the best-performing model for final deployment.

30. Linear Regression Performance Mean Absolute Error (MAE) : [25121.61954347192] Average of absolute differences between predicted and actual prices. Root Mean Squared Error (RMSE) : [39652.79061526264] Penalizes larger errors more heavily — reflects overall prediction quality. R² Score : [0.7950095261783562] Indicates how well the model explains the variance in house prices. (Closer to 1 means better performance) Observation : Linear Regression served as a simple baseline but may underperform on complex, non-linear data.

31. Ridge Regression Performance Mean Absolute Error (MAE) : [25115.797346245272] Average absolute difference between predicted and actual prices. Root Mean Squared Error (RMSE) : [39651.3300225149] Penalizes larger errors more significantly, giving insight into prediction quality. R² Score : [0.7950246273644516] Represents how well the model explains the variance in the target variable. (Closer to 1 = better fit) Observation : Ridge Regression provided a good balance between bias and variance , reducing overfitting compared to standard Linear Regression.

32. Random Forest Performance Mean Absolute Error (MAE) : [19534.81369169928] Indicates the average magnitude of prediction errors. Root Mean Squared Error (RMSE) : [30077.850799880194] Reflects overall prediction accuracy — lower is better. R² Score : [0.8820549368692863] Measures how well the model explains the variance in the target variable. (Closer to 1 = better fit) Observation : Random Forest provided the best balance of accuracy and robustness , making it the most suitable model for deployment.

33. Model Comparison Table Model MAE RMSE R² Score Linear Regression 25121.61954347192 39652.79061526264 0.7950095261783562 Ridge Regression 25115.797346245272 39651.3300225149 0.7950246273644516 Random Forest 19534.81369169928 30077.850799880194 0.8820549368692863

34. Best Model Selection Random Forest Regressor delivered the best performance among all tested models. Evaluation Highlights : Achieved the lowest RMSE , indicating minimal prediction error Scored the highest R² , showing strong ability to explain price variance Why Random Forest? Handles non-linear relationships effectively Robust to outliers and feature interactions Less prone to overfitting due to ensemble nature Selected as the final model for deployment

35. Residual Plot Explanation What It Shows : Plots the residuals — the difference between actual and predicted values. Ideal Pattern : A good model will show residuals scattered randomly around the horizontal axis (zero line), indicating no clear pattern. Why It Matters : Detects bias in predictions Reveals potential non-linear trends or model errors Helps evaluate if model assumptions are met

36. Residual Plot Image

37. Actual vs Predicted Plot Explanation Purpose : Visualize how closely the model’s predicted values match the actual sale prices . Ideal Outcome : Points lie along the 45° diagonal line , indicating perfect predictions. This line represents: Predicted = Actual Interpretation : Closer to the line → more accurate prediction Farther from the line → greater prediction error Why It’s Useful : Helps visually assess the model's overall performance and detect any systematic errors .

38. Actual vs Predicted Plot Output

39. Model Saving & Output Files Model Saved As : ' trained_model.pkl ' — using joblib for efficient serialization Test Data Files : 'test.csv' — contains test set used for evaluation 'house_price_submission.csv' — formatted predictions for submission/output Saved Visualizations : 'residual_plot.png' 'predicted_vs_actual.png'

40. Evaluation Summary Random Forest Regressor Achieved the lowest MAE and RMSE Best at capturing complex, non-linear relationships Selected for final deployment Ridge Regression Helped reduce overfitting compared to Linear Regression Performed better than basic Linear model on validation data Linear Regression Simple and interpretable Least accurate among the models due to its inability to handle non-linearity

41. Challenges Faced Missing Values in Dataset Some features had a significant number of null entries Required careful cleaning and dropping of affected rows Feature Correlation & Redundancy Certain features were highly correlated Needed to avoid multicollinearity and overfitting by selecting only the most relevant ones Complexity in Visualization Making graphs interpretable for both technical and non-technical users Balancing detail with clarity when plotting multiple relationships

42. How the Challenges Were Solved Handled Missing Values ➤ Used . dropna () to remove rows with null values in key features, ensuring clean and reliable data for training. Reduced Feature Redundancy ➤ Manually selected the top 7 most relevant features based on correlation and domain understanding to avoid overfitting. Simplified Visualization ➤ Utilized Seaborn for clear and informative plots like histograms, KDEs, and heatmaps to effectively communicate insights.

43. SECTION 4: DEPLOYMENT & FRONTEND

44. What is Streamlit ? Streamlit is an open-source Python library used to create interactive web applications for data science and machine learning projects. Key Features : Simple and intuitive syntax Converts Python scripts into beautiful dashboards Ideal for real-time ML model interaction and data visualization Why Streamlit ? No need for front-end knowledge Fast to build, easy to deploy Perfect for showcasing ML models to users

45. Streamlit App Layout Sidebar ➤ Contains all user input fields (e.g., area, bedrooms, bathrooms, etc.) ➤ Built using st.sidebar.slider () , st.sidebar.selectbox () , etc.

46. Main Area ➤ Displays the predicted house price ➤ Shows visual graphs and plots based on user input

47. Sidebar Inputs The Streamlit sidebar contains interactive input fields for all 7 selected features : Feature Inputs (via sliders/text fields): OverallQual – Slider (1 to 10) GrLivArea – Slider (e.g., 500 to 4000 sq ft) GarageCars – Selectbox or slider (0 to 4) GarageArea – Slider (e.g., 0 to 1500 sq ft) TotalBsmtSF – Slider (e.g., 0 to 2000 sq ft) FullBath – Selectbox (1 to 4) YearBuilt – Slider (e.g., 1900 to 2023) Streamlit Widgets Used : st.sidebar.slider () st.sidebar.selectbox () st.sidebar.number_input () Purpose : Allow users to customize inputs and get real-time predictions from the trained model.

48. Sidebar Inputs Code

49. Loading Model in Streamlit App Model File : trained_model.pkl — saved using joblib.dump () Purpose : Allows the Streamlit app to use the pre-trained model without retraining Enables real-time predictions based on user inputs How It’s Loaded :

50. Predicting from User Input Trigger: User clicks the “Predict Price” button in the sidebar Input Collection: Collects user-provided values from sidebar widgets (like sliders or number inputs), then forms a single-row DataFrame :

51. Prediction Execution: The trained model makes a prediction on the input: Result Display: Prediction is shown in a success message with formatting:

52. Displaying Graphs in Streamlit Matplotlib Used: Plots created using matplotlib.pyplot Imported Figures: Graphs like Actual vs Predicted, feature importance, etc. were generated and saved as fig Displayed with: Graphs enhance understanding of model performance and user input impact.

53. Example Graphs on UI

54. Example Graphs on UI

55. Summary of Deployment Clean and Interactive Interface – Easy-to-use layout with sidebar inputs and real-time output No Need for HTML / JavaScript – Built entirely with Python using Streamlit – Simplifies dashboard creation for ML apps ML Served Instantly on User Input – Trained model responds to new data – Predictions displayed instantly with visuals

56. SECTION 5: CONCLUSION & FUTURE WORK

57. Key Takeaways ML Models Can Predict Prices Accurately – With proper features and tuning, predictions are reliable Visualizations Improve Understanding – Graphs like residual plots and scatter plots reveal insights Streamlit Makes Sharing Simple – No web dev needed – just Python – Easy deployment for end-users

58. Strengths of the Project End-to-End Pipeline – Covers everything from data preprocessing to deployment Deployed Machine Learning Model – Live predictions through a Streamlit web app Feature Explainability – Visuals and plots explain how features affect price Reusable & Scalable – Can be extended with more data or features easily

59. Limitations Limited Dataset Scope – Based only on Ames Housing data; may not generalize to other regions No Geolocation Features Used – Location-based variables like neighborhood coordinates not included No Time-Series or Market Trends – Model doesn't account for changing market conditions over time

60. Future Work: Enhancing Features Include More Relevant Columns – Add features like LotArea , Neighborhood , MSZoning , etc. One-Hot Encoding of Categorical Variables – Properly handle non-numeric data to improve model accuracy Feature Expansion = Better Predictions – Richer inputs help capture real-world complexity

61. Future Work: Map & Geo Analysis Integrate Latitude & Longitude – Add location coordinates to better capture neighborhood influence Create Price Heatmaps – Visualize property value distribution across different areas Improve Location-Specific Predictions – Enhance model accuracy by combining spatial and feature-based data

62. Future Work: Batch Prediction Upload CSVs for Bulk Prediction – Allow users to input multiple house records at once Export Predicted Results – Download results as CSV or Excel for further analysis Enhance Usability for Businesses & Analysts – Make the tool scalable for real estate agencies and data teams

63. Learning Outcomes Built a Complete ML Pipeline – From data preprocessing to model evaluation and saving Deployed with Streamlit – Created an interactive and user-friendly web app Understood Regression Models – Compared performance using MAE, RMSE, and R² metrics Handled Real-World Data Challenges – Managed missing values, feature selection, and visual analysis

64. Real-World Impact Empowers Homeowners, Buyers & Sellers – Provides quick and accurate price estimates for informed decisions Assists Real Estate Professionals – Supports agents and evaluators with data-backed insights Scalable to Other Cities & Regions – Model can be retrained with different city datasets for broader use Bridges Tech & Real Estate – Demonstrates how machine learning can solve industry problems

65. References https://scikit-learn.org https://docs.streamlit.io https://seaborn.pydata.org https://pandas.pydata.org Kaggle: Housing Dataset