This document discusses predicting house prices in Tehran, Iran using housing data. It explores factors that impact price like size, area, location, bedrooms and bathrooms. The dataset has 3479 records and 8 variables, including categorical variables like parking and continuous variables like area and price. The proposed solution is to process the dataset, build a regression model, train it and test it to make predictions. Implementation steps include data cleaning, feature engineering, outlier removal, model building, testing the regression model and making predictions to evaluate accuracy. Linear regression is used, which relates price positively to rooms, address and area, but negatively to parking. The model has 70% accuracy in predicting relationships. Issues discussed include accuracy, transparency, fairness and privacy.

1. HOUSE RICE
PREDICTION
- IRAN
STUDENT ID
UNIVERSITY NAME
DATE

2. PROBLEM
DEFINITION
• Predicting the House Prices in Tehran City
located in Iran using the Housing Data.
• The prediction of house price is impacted by
various factors such as size, total area in square
feet, location, total number of bedrooms and
number of bathrooms.

3. DATA
EXPLORATION
• The dataset has 3479 records and 8 variables.
• There are 4 categorical variables such as
parking, warehouse, address and elevator and 4
continuous variables such as area, room, price
in Tomar and price in USD

4. PROPOSED SOLUTION
Dataset
processing
Regression
Model building
Training the data
for Regression
model
Testing the
dataset
prediction

5. DATASET

6. IMPLEMENTATION
STEPS
1. Importing Data
2. Data Cleaning and data preprocessing
3. Feature Engineering
4. Removal of Outliers
5. Model Building
6. Testing the regression model
7. Prediction and accuracy of the model

7. 1) DATA CLEANING STEPS
1) Dropping
Unnecessary
Columns
2) Filling missing
values in columns
3) Transforming the
categorical variables
into numerical by
encoding
4) Creating a value
for address column
to store the location
of address
5) Cleaning Area in
Sq_Ft column

8. 2) FEATURE ENGINEERING STEPS
The Boolean data for three
columns such as parking,
elevator and parking was
changed to integer to
facilitate model building
The variable address was
changed to numeric since
location plays an effective
role in the analysis
Add value for address and
room per area

9. 3) OUTLIER DETECTION AND REMOVAL
Removal of Houses
where area above 1000
square feet for same
location
Removing houses with
missing values on
address
Data visualization
Performing One Hot
Encoding

10. 3) OUTLIER DETECTION AND REMOVAL

11. 4) MODEL BUILDING
Use Measure accuracy and prediction
Build Build the model.
Split Split the data into Training and Testing.
Split Split the data into columns which are dependent and independent.

12. 5) TESTING REGRESSION MODELS AND PREDICTION OF HOUSE PRICE
USE REGRESSION TO
TEST THE DATA.
PREDICT THE PRICE.

13. DESCRIPTIVE STATISTICS

14. DATA VISUALIZATION

15. A) LINEAR REGRESSION
• Linear regression attempts to model the relationship between two variables by fitting a linear
equation to observed data. One variable is considered to be an explanatory variable, and the
other is considered to be a dependent variable.
• The most common method for fitting a regression line is the method of least-squares. This
method calculates the best-fitting line for the observed data by minimizing the sum of the
squares of the vertical deviations from each data point to the line
• A linear regression line has an equation of the form Y = a + bX, where X is the explanatory
variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the
value of y when x = 0).

16. SCATTER PLOT

17. HOUSE PRICE
PREDICTION

18. REGRESSION
• The price is positively related to the total number of rooms, value of the address and total
area in square feet.
• The house price is negatively related to the value of parking
• The variance and R squared value is 0.69 which means this model is 70% accurate
in predicting the relationship

19. ISSUES INVOLVED

20. Professional Issues:
• Accuracy
• Transparency

21. Ethical Issues:
• Fairness
• Privacy

22. THANK YOU