Running head: DATA ANALYSIS.
Data Analysis.
Student Name.
Institution Affiliation.
Date.
, DATA ANALYSIS.
Data Analysis.
Boston Housing Dataset.
This is the dataset that was collected by the United states government during census. to find out
the price of residential houses in Boston in the united states. The dataset was originally part of
UCI machine learning repository. The dataset contains 506 samples and 13 features. The main
goal while using this dataset is to predict the prices of residential houses using a specific feature.
For this dataset, we are going to perform explorative data analysis and, on the machine learning
section we are going to use linear regression.
Explorative Analysis of the Dataset.
This is a vital step done before training the model, to understand the relationships. Virtualization
is very important; it helps us understand the relationship between features and target variables
(Bonaccorso, 2017). Below is an explorative analysis of the data
Data Analysis.
Student Name.
Institution Affiliation.
Date.
, DATA ANALYSIS.
Data Analysis.
Boston Housing Dataset.
This is the dataset that was collected by the United states government during census. to find out
the price of residential houses in Boston in the united states. The dataset was originally part of
UCI machine learning repository. The dataset contains 506 samples and 13 features. The main
goal while using this dataset is to predict the prices of residential houses using a specific feature.
For this dataset, we are going to perform explorative data analysis and, on the machine learning
section we are going to use linear regression.
Explorative Analysis of the Dataset.
This is a vital step done before training the model, to understand the relationships. Virtualization
is very important; it helps us understand the relationship between features and target variables
(Bonaccorso, 2017). Below is an explorative analysis of the data
