MODELING & COMPUTING TECHNIQUES
Added on 2022-08-24
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Running head: MODELING & COMPUTING TECHNIQUES
Modeling & Computing Techniques
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Modeling & Computing Techniques
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2MODELING & COMPUTING TECHNIQUES
Executive Summary
Machine learning and Artificial Intelligence is considered to be one of the leading and powerful
technologies used in the recent world. The most important part is that human’s haven’t seen the
full potential of such technologies. This is because the system has the ability to learn automatically
from historical data and from past experience. Machine learning technology are generally used to
transform information into knowledge. Machine learning models are used to gather useful
information and the hidden patterns inside the data and make decisions based on the data with
minimum human involvement.
The dataset used in the analysis contains information of each and every building like home,
apartment etc. which are sold in the New York City property market over the period of 12 months.
The dataset contains information of five different places. A total number of 84548 numbers of
information are present into the data file.
The model used for classifying the sales is artificial neural network model using deep
learning. Basically Keras Regressor algorithm is used to train the training dataset and will be tested
over the tested dataset as how well the classifier classified the target variable values. It can be said
that deep learning or the neural network models provides better prediction rate as compare to other
model.
In the analysis a deep thorough analysis, data exploration, visualization and at the end
prediction has been performed to get in-depth knowledge of the dataset. Proper machine learning
model with keras layers and tensorflow in the backend has been developed using neural network
techniques. At the end a conclusion will be concluded on how the model predicts the sale price
and different hidden patterns and information will be drawn in the end.
Executive Summary
Machine learning and Artificial Intelligence is considered to be one of the leading and powerful
technologies used in the recent world. The most important part is that human’s haven’t seen the
full potential of such technologies. This is because the system has the ability to learn automatically
from historical data and from past experience. Machine learning technology are generally used to
transform information into knowledge. Machine learning models are used to gather useful
information and the hidden patterns inside the data and make decisions based on the data with
minimum human involvement.
The dataset used in the analysis contains information of each and every building like home,
apartment etc. which are sold in the New York City property market over the period of 12 months.
The dataset contains information of five different places. A total number of 84548 numbers of
information are present into the data file.
The model used for classifying the sales is artificial neural network model using deep
learning. Basically Keras Regressor algorithm is used to train the training dataset and will be tested
over the tested dataset as how well the classifier classified the target variable values. It can be said
that deep learning or the neural network models provides better prediction rate as compare to other
model.
In the analysis a deep thorough analysis, data exploration, visualization and at the end
prediction has been performed to get in-depth knowledge of the dataset. Proper machine learning
model with keras layers and tensorflow in the backend has been developed using neural network
techniques. At the end a conclusion will be concluded on how the model predicts the sale price
and different hidden patterns and information will be drawn in the end.
3MODELING & COMPUTING TECHNIQUES
Table of Contents
Executive Summary ........................................................................................................................ 2
Introduction ..................................................................................................................................... 4
Discussion ....................................................................................................................................... 4
Introduction and observation of the dataset ................................................................................ 4
The proposed model for price prediction .................................................................................... 8
Conclusion .................................................................................................................................... 10
References ..................................................................................................................................... 11
Appendix ....................................................................................................................................... 13
Table of Contents
Executive Summary ........................................................................................................................ 2
Introduction ..................................................................................................................................... 4
Discussion ....................................................................................................................................... 4
Introduction and observation of the dataset ................................................................................ 4
The proposed model for price prediction .................................................................................... 8
Conclusion .................................................................................................................................... 10
References ..................................................................................................................................... 11
Appendix ....................................................................................................................................... 13
4MODELING & COMPUTING TECHNIQUES
Introduction
Machine learning and Artificial Intelligence is considered to be one of the leading and
powerful technologies used in the recent world (Alpaydin, 2020). The most important part is that
human’s haven’t seen the full potential of such technologies. This is because the system has the
ability to learn automatically from historical data and from past experience. Machine learning
technology are generally used to transform information into knowledge (Bishop, 2006). Machine
learning models are used to find the hidden patterns inside the data and make decisions based on
the data with minimum human involvement (Moolayil, Moolayil & John, 2019). Mainly there are
2 types of machine learning algorithm categories mainly supervised and unsupervised learning
(Brownlee, 2016).
In supervised learning the inputs of the dataset is known and the dataset contains labelled
data with known output, whereas in unsupervised learning the input is known but the dataset
contains, un-labelled data with unknown outputs (Campesato, 2020). In this analysis the target
variable is the sale price attribute and the goal is to predict the sales price using artificial neural
network using deep learning methods (Chernick, 1998).
Deep learning is another field or it can be said that it is a subpart of machine learning which
consist of network based layers and capable of learning from the unsupervised data which are
generally unstructured and unlabeled (Daniel, 2013). There are different kinds of layers used to
build a neural network model. For this analysis only dense layer has been used to build the artificial
neural network model (Dietterich, 1997).
The accuracy and the performance of the models also depends on the data. If the data
contains more missing values or null values then the model will not be able to classify properly as
the data is not a good fit for the model (Géron, 2019). The more cleanly the data the more acutely
the model will classify the target variables. It has been seen that using deep learning more accurate
result has been observed instead of using older learning algorithms (Mitchell, 1997).
Discussion
Introduction and observation of the dataset
Exploring the attributes of the dataset:
1. BOROUGH: The Borough attribute consist of 5 different classes which are basically five
location where properties have been sold which are basically, 1 for 'Manhattan', 2 for
'Bronx', 3 for 'Brooklyn', 4 for 'Queens' and 5 for 'Staten Island' and these should be
considered to be categorical values.
2. NEIGHBORHOOD: This attribute tells the neighborhood name for the particular
properties. The name is given by the department of finance assessors also the name is
similar to the name of the Finance designates. Also it can be seen that there may be few
differences in the neighborhood attributes and few sub- neighborhood might not be include
also with respect to the value of the attribute the attribute will be categorical.
3. BUILDING CLASS CATEGORY: This attribute is used to identify similar properties of
the Rolling sales files without having look into individual building classes (Norris, 2020).
Introduction
Machine learning and Artificial Intelligence is considered to be one of the leading and
powerful technologies used in the recent world (Alpaydin, 2020). The most important part is that
human’s haven’t seen the full potential of such technologies. This is because the system has the
ability to learn automatically from historical data and from past experience. Machine learning
technology are generally used to transform information into knowledge (Bishop, 2006). Machine
learning models are used to find the hidden patterns inside the data and make decisions based on
the data with minimum human involvement (Moolayil, Moolayil & John, 2019). Mainly there are
2 types of machine learning algorithm categories mainly supervised and unsupervised learning
(Brownlee, 2016).
In supervised learning the inputs of the dataset is known and the dataset contains labelled
data with known output, whereas in unsupervised learning the input is known but the dataset
contains, un-labelled data with unknown outputs (Campesato, 2020). In this analysis the target
variable is the sale price attribute and the goal is to predict the sales price using artificial neural
network using deep learning methods (Chernick, 1998).
Deep learning is another field or it can be said that it is a subpart of machine learning which
consist of network based layers and capable of learning from the unsupervised data which are
generally unstructured and unlabeled (Daniel, 2013). There are different kinds of layers used to
build a neural network model. For this analysis only dense layer has been used to build the artificial
neural network model (Dietterich, 1997).
The accuracy and the performance of the models also depends on the data. If the data
contains more missing values or null values then the model will not be able to classify properly as
the data is not a good fit for the model (Géron, 2019). The more cleanly the data the more acutely
the model will classify the target variables. It has been seen that using deep learning more accurate
result has been observed instead of using older learning algorithms (Mitchell, 1997).
Discussion
Introduction and observation of the dataset
Exploring the attributes of the dataset:
1. BOROUGH: The Borough attribute consist of 5 different classes which are basically five
location where properties have been sold which are basically, 1 for 'Manhattan', 2 for
'Bronx', 3 for 'Brooklyn', 4 for 'Queens' and 5 for 'Staten Island' and these should be
considered to be categorical values.
2. NEIGHBORHOOD: This attribute tells the neighborhood name for the particular
properties. The name is given by the department of finance assessors also the name is
similar to the name of the Finance designates. Also it can be seen that there may be few
differences in the neighborhood attributes and few sub- neighborhood might not be include
also with respect to the value of the attribute the attribute will be categorical.
3. BUILDING CLASS CATEGORY: This attribute is used to identify similar properties of
the Rolling sales files without having look into individual building classes (Norris, 2020).
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