ppt for housing price

1. Housing
Price
Prediction

2. Topics Covered
01
Language
Python
IDEs
Jupyter Notebook,
VS Code and PyCharm
04
Libraries
02
Model Building
Sklearn for model
building
03
Connect the model
Python Flask for HTTP
server
05
Front End
HTML, CSS, JAVASCRIPT
for UI
06
Numpy and Pandas
for data cleaning
Matplotlib for data
visualization

3. Introduction
Welcome to the presentation on House Price Prediction using Data Science.
In this presentation, we will delve into the entire process of predicting house prices.
We'll cover everything from data processing to building machine learning models and
creating a user-friendly interface.
Python in Data Science
Python is a versatile and widely-used programming language in data science.
Its rich ecosystem of libraries makes it ideal for tasks like data processing,
visualization, and modeling.

4. Data Cleaning
Data cleaning is the critical first step in any data analysis project.
We used Pandas, a powerful Python library, for data cleaning tasks.
Examples include handling……
• missing values
• removing duplicates
• transforming data for analysis.

5. Some functions used
for data cleaning
Data Cleaning Methods
The drop() removes
specific row and column
drop()
Returns True or False
for Null values
isnull()
Returns description of
data like std, min, max etc
describe()
Removes rows that
contain null values
dropna()

6. Some functions used
for data cleaning
Feature Engineering
apply ()
describe ()
loc [ ]
lambda
allow the users to pass a function
and apply it on every single value
of the Pandas series.
attribute access a group of rows
and columns by label or a
boolean array.
The describe() method returns
description of the data in the
DataFrame.
A lambda function is a small
anonymous function, lambda take
any number of arguments, but can
only have one expression.

7. Outlier Removal
An Outlier is a data-item/object that deviates significantly from the rest of the (so-
called normal)objects. They can be caused by measurement or execution errors. The
analysis for outlier detection is referred to as outlier mining.
There are many ways to detect the outliers, and the removal process is the data
frame same as removing a data item from the panda’s data frame.
Here pandas data frame is used for a more realistic approach as in real-world project
need to detect the outliers arouse during the data analysis step, the same approach
can be used on lists and series-type objects.

8. One Hot Encoding
One hot encoding is a technique that we use to represent
categorical variables as numerical values in a machine
learning model.
The advantages of using one hot encoding include:
• It allows the use of categorical variables in models that
require numerical input.
• It can improve model performance by providing more
information to the model about the categorical variable.
• It can help to avoid the problem of ordinality, which can
occur when a categorical variable has a natural ordering
(e.g. “small”, “medium”, “large”).

9. Accuracy of our model: 0.8629132245229449
Model Building
SKlearn
Scikit-Learn is a comprehensive machine learning library.
We have used sklearn library for training and testing data.
We will use Linear Regression to predict accuracy of our
model.

10. Integrated Development Environment
01
Offers an interactive
environment for data
exploration
02
Visual Studio Code
Provides versatlie
coding platform
03
PyCharm
Offers advance features for
Python development
04
Postman
Used for HTTP Testing
Jupyter Notebook

11. app.run(host, port, debug, options)
Host:The default hostname is 127.0.0.1, i.e. localhost.
Port: The port number to which the server is listening
to. The default port number is 5000.
Debug: The default is false. It provides debug
information if it is set to true.
Options: It contains the information to be forwarded to
the server.
Python Flask for HTTP Server
Python
Flask
route():defines the URL mapping of the associated
function.
app.route(rule, options)
rule: It represents the URL binding with the
function.
options: It represents the list of parameters to be
associated with the rule object

12. UI Design
HTML
Used to make layout
of the page
01 CSS
Used to design page
and make it more
attractive to user.
02
JavaScript
Used to add
functionalities in Web
page.
03

13. Demo Video

14. Thank You !
Presented By : Krish Patel (IU2041050078)
Devarsh Patel (IU2041050074)
Kaksh Patel (IU2041050077)