This document outlines a project to develop a machine learning model to predict house prices in the United States. It describes the company developing the system, provides background on machine learning and the problem domain, and outlines the objectives, requirements, methodology, design, and expected results of the project. The proposed methodology involves collecting house data, preprocessing it, training a random forest model on 80% of the data and testing it on the remaining 20%, and using the trained model to predict house prices. The system is intended to help buyers search for homes within their budget and avoid being misled on prices.

1. CONTENTS
Company Profile
Domain details
Project detail
Objective
System requirement
Proposed methodology
System design
Conclusion

2. COMPANY PROFILE
 ICann Corp is a Bangalore based technology and IT firm
focused on delivering the best quality and cost effective
solutions to clients.
 The company considers IT as the backbone of a business eco-
system and provides complete solutions to address all aspects
of the business.
 The company’s approach focuses on new ways of business,
combining IT innovation and adoption while also an
organizations current IT assets which is the business and
technology strategies in today’s environment.
 The company provides highly customized application
development and integration services.

3. DOMAIN DETAILS
 Machine learning is an application of artificial
intelligence (AI) that provides systems the ability to
automatically learn and improve from experience
without being explicitly programmed.
 Machine learning focuses on the development of
computer programs that can access data and use it
to learn for themselves.
 Machine learning algorithms are used in a wide
variety of applications, such as in medicine, email
filtering, speech recognition, and computer vision.

4. MACHINE LEARNING APPROACH
 Supervised learning: Supervised learning algorithms build a
mathematical model of a set of data that contains both the inputs
and the desired outputs .The data is known as training data, and
consists of a set of training examples.
ex: face detection, signature recognition.
 Unsupervised learning : Unsupervised learning algorithms take a
set of data that contains only inputs, and find structure in the data,
like grouping or clustering of data points .
ex: fraud detection , anomaly detection

5.  Reinforcement learning: Reinforcement learning is
an area of Machine Learning. It is about taking
suitable action to maximize reward in a particular
situation. In the absence of a training dataset, it is
bound to learn from its experience.
ex: gaming.

6. PROJECT ASSIGNED
“HOUSE PRICE PREDICTION SYSTEM”
ABSTRACT
 House price prediction can help the developer
determine the selling price of a house and this also
helps the customer to purchase a house at right time.
 There are three factors that influence the price of a
house which include physical conditions, concept
and location.
 In this project, we have developed and evaluated the
performance and the predictive power of a model
trained and tested on data collected from houses in
united states.

7. OBJECTIVES
 The main aim of this project is to
predict sale prices for homes in united
states.
To enable users to search home as per
the budget.
To provide best price to user without
getting cheated.

8. SYSTEM REQUIREMENT
HARDWARE REQUIREMENTS
Processor :any processor above 500MHZ
Ram :4GB
Hard Disk :250GB
Input device :standard keyboard and mouse
Output device :high resolution monitor
SOFTWARRE REQUIREMENTS
Operating system :windows 7 or higher
Programming : python 3.6 and related libraries

9. PROPOSED METHODOLOGY
 DATA COLLECTION: we have collected data for house price
prediction from kaggle an online website and stored in excel file.
 DATA PREPROCESSING: Data preprocessing involves
transforming raw data into a more coherent format. The data
pre-processing also involves in checking out for missing values
and it also splits the data-set into training and testing the
dataset.
 TRAINING THE MODEL : In this phase, data is broken down
into two part: Training and Testing. There are 80% of data is used for
training purpose and reaming 20% used for testing purpose. The model
is trained by using random forest algorithms as it is best fit for
prediction.

10. PROPOSED METHODOLOGY
 Random Forest works in two-phase first is to create the random
forest by combining N decision tree, and second is to make
predictions for each tree created in the first phase.
 Step 1: From the dataset pick N random records.
 Step 2: Based on N records, build a decision tree.
 Step 3a: From algorithm, choose the number of trees and repeat steps 1
and 2.
 Step 3b: In case of a regression problem, for a new record, each tree in
the forest predicts a value for the output.
 TESTING THE MODEL :Finally, the trained model is applied to test
dataset and house price is predicted.

11. SYSTEM DESIGN
FIG:THE GENERIC FLOW OF DEVELOPMENT

12. RESULT SNAPSHOT

13. RESULT

14. RESULT

15. CONCLUSION
 This project entitled “House Price Prediction
System.” is useful in buying the houses, by predicting
house prices, and thereby to guide their buyers
accordingly. The proposed system is also useful to
the buyers to predict the cost of house according to
the area it is present.

16. THANK YOU….