Predicting-Housing-Prices-with-Multiple-Regression-and-Machine-Learning

This project explores the use of multiple regression and machine learning techniques to predict housing prices with greater accuracy. By analyzing various factors such as property features, location specifics, and economic indicators, the model provides a reliable estimate of property values. This approach not only enhances the accuracy of real estate valuations but also offers valuable insights into the key determinants of housing prices. As the real estate market continues to evolve, such advanced predictive models will be indispensable tools for stakeholders aiming to make informed decisions based on reliable data. We employ multiple regression to establish a foundational model, identifying key predictors and quantifying their impact on housing prices. This step involves:

Data preprocessing (handling missing values, normalizing data)
Feature selection (using statistical tests to identify significant variables)
Model fitting (training the regression model on the dataset)

1. Predicting
Housing Prices
with Multiple
Regression and
Machine Learning

2. INTRODUCTION
•Have you ever wondered what truly drives
home prices in your neighborhood?
•Using the power of multiple regression analysis and
modern machine learning techniques, we'll dive deep
in the layers and uncover the hidden factors that
influence real estate values.
•From data cleaning to model building, you'll see firsthand
how we can use the power of data to make smarter, more
informed decisions.

3. Problem Statement
A big challenge is to develop a reliable model that can
accurately predict housing prices based on various
factors. This will help real estate professionals,
investors, and homebuyers make informed decisions
when buying, selling, or investing in properties.

4. Objectives of the Study
1
Assess Regression
Evaluate the validity of multiple regression for
housing price prediction.
2
Use ML
Explore how machine learning can enhance
predictive accuracy.
3
Real-world Application
Apply the methods to a real housing dataset.
4
Provide Insights
Offer recommendations for real estate stakeholders.

6. Mathematical Model
1 Data Cleaning Process
2 Regression Analysis in Excel
3 Python Regression Modeling

7. Why Multiple Regression Analysis?
1
Identify Factors
Analyze how factors like area, rooms, and bathrooms impact
housing prices.
2
Build Model
Use regression to create a predictive model based on the
data.
3
Evaluate Performance
Assess the model's accuracy in forecasting housing prices.

8. DataCleaningProcess
Drop Irrelevant
Remove columns that don't impact housing prices.
Handle Missing Values
Impute or delete rows with missing values.
Detect Outliers
Identify and adjust or remove extreme outliers.
Prepare for Analysis
Ensure the data is clean and ready for modeling.

9. Outliers
An outlier is an extremely high or low
data value when compared with the
rest of the data values. These values
are out of the range from the data
set.
Example:Consider the heights of students in a class (in
cm): [140, 142, 143, 146, 148, 151, 153, 350]
Here, 350is an outlier because it is much taller
compared to the other heights.

10. Steps to Find Outliers
A data value less than Q1 –1.5 (IQR) or greater than Q3 + 1.5 (IQR) can be considered
an outlier.
Steps:
Arrange the data in ascending order and find Q1 and Q3.
Find the Interquartile Range Q3 –Q1.
Multiply IQR by 1.5.
Subtract step 3 from Q1 and add in Q3.
Check the data set for outliers.

11. Steps to Find Outliers
Arrange the Data in Ascending Order:
140,142,143,146,148,151,153,350
•Find Q1 and Q3:
Q1 (First Quartile): The median of the first half of the data
140,142,143,146
Median of the first half (Q1) = (142+143)/2= 142.5
Q3 (Third Quartile): The median of the second half of the data
148,151,153,350
Median of the second half (Q3) = (151+153)/2= 152

12. Steps to Find Outliers
Find the Interquartile Range (IQR):
??????��=�3−�1
= 152−142.5
= 9.5
Multiply IQR by 1.5:
1.5×??????��= 1.5×9.5
= 14.25

13. Steps to Find Outliers
Subtract Prev. Step from Q1 and Add
in Q3:
Lower Bound:
�1−1.5×??????��=142.5−14.25
= 128.25
Upper Bound:
�3+1.5×??????��=152+14.25
= 166.25
Check the Data Set for Outliers:
Any value below 128.25or above 166.25is an
outlier.
In this dataset, 350is above 166.25, so it is an
outlier.

14. BOX PLOT
A box plot is a simple visual tool that summarizes data using a box and whiskers.
It shows the minimum, first quartile (Q1), median, third quartile (Q3), and maximum values.
It helps identify the spread and any potential outliers in the dataset.

15. BOX PLOT (price, area)

16. Regression Analysis in Excel
Scatter Plots
Visualize relationships between
variables.

17. Python Regression Modeling
Data Preparation
Load and clean the data using Pandas.
Model Training
Train a regression model with Scikit-
Learn.
Data Visualization
Use Seaborn for exploratory analysis.
Initial Results
Achieve an initial accuracy of 54%.

18. PRACTICAL DEMONSTRATION
OF TRAINING A MODEL

19. Conclusion and Recommendations
Key Findings
The model achieved 54% initial accuracy, with room for improvement.
Future Work
Incorporate more features, use advanced techniques, and optimize the model.
Stakeholder Insights
Provide actionable recommendations for real estate agents and policymakers.
Impact
The study expands knowledge on data-driven housing price prediction.