The Point of Analysis: Defining the Problem Context

In order to conduct an effective analysis, it is essential to define the problem in context. The question at hand is what is likely to sell for a given house on the market with a listed price and a series of attributes describing the home. This can be interpreted in multiple ways, which is why it's crucial to take the time to formally define the problem.

For this article, let us assume we are real estate tycoons looking for the next best investment opportunity for a given house on the market with a listed price and a series of attributes describing the home. We want to predict how much our house will actually sell for, also known as the sales closed price. The data set we have is a sample of homes that sold over the course of 2017 using this sample, we are to provide a quick proof of concept of whether it's worth investing in more data for the 5.5 million homes that sold in the U.S. in 2017.

Understanding the Limitations of the Data

Before we can begin analyzing the data, we need to understand some of its limitations. Firstly, we only have a small geographical area, which means that if we apply our model to new areas, it will pose a serious risk. Secondly, we know that we only have residential data, so we shouldn't expect to predict how much a business location is worth. Lastly, we only have one year's worth of data, which will make it hard to draw strong conclusions about the seasonality in the data set.

Despite these limitations, we have identified around 50 attributes that our client believes are likely to influence the price of a home. These attributes generally fall into several groups. For dates, we have "date listed" and the year the home was built. For locational data, we have the city that the home is in, its school district, and its actual postal address. We also have many different metrics to gauge the size of a home, such as the number of bedrooms and bathrooms, as well as the area of living space. For prices, we have the listing price and the sales price. We also have a lot of data available on the amenities that a house has, such as a pool or garage, as well as the construction materials that were used to build the house.

The Importance of Data Validation

Big data can be overwhelming, and it's easy for things to go wrong when loading data. It's essential to make sure you have the right number of Records and columns. We can use the `DF count` function to get the row count, `DF columns` to get the list of columns, and take the length of `DF columns` to get the number of poems (i.e., data points).

We also need to set the data types for all of our fields, which is a huge advantage over CSV files. We can use D-types on our data frame to create a list of tuples containing a column name and its corresponding data type. It's still worth checking, especially if you're not the one defining it.

Verifying Our Data

In this video, we learned about the data set we will be using and the problem we will be trying to solve. We also learned how to check to see if our data loaded properly by checking rows, columns, and data types. Now it's your turn to apply what you've learned in the exercises to verify our data.

In this article, we will fully develop each part of the transcription into a readable paragraph or section, without condensing or summarizing the content.

"WEBVTTKind: captionsLanguage: enwhat's the point of doing an analysis if you aren't solving the right problem in this video we'll define our problem in the context of our data we were going to build a model to predict how much our house sells for this question can be interpreted in multiple ways which is why it's important to take the time to formally define it let's assume we are real estate tycoons looking for the next best investment opportunity for a given house on the market with a listed price and a series of attributes describing the home what is it likely to actually sell for aka the sales closed price the data set we have it is a sample of homes that sold over the course of 2017 using the sample we are to provide a quick proof of concept of whether it's worth investing in more data for the 5.5 million homes that sold in the u.s. in 2017 to do this we need to understand some of the limitations of the data we have first we only have a small geographical area so to apply our model to new areas pose a serious risk we know that we only have residential data so we shouldn't expect to predict how much a business location is worth lastly we only have one year's worth of data which will make it hard to draw strong conclusions about the seasonality in the data set the original data set has hundreds of attributes available but in order to start simple we've already worked with our client to identify around 50 attributes they think are likely to influence the price of a home these attributes generally fall into these groups for dates we have date listed and the year the home was built for a locational data we have the city that the home is in the its school district and it's actual postal address we also have many different metrics to gauge the size of a home like number of bed and bathrooms as well as the area of living space for prices we have the listing price and we would be able to predict anything without the sales price we also have a lot of data available on the amenities that a house has like a pool or garage as well as the construction materials that were used to build the house big data means a lot can go wrong when loading data make sure you have the right number of Records and columns we can use DF count to get the row count DF columns to get the list of columns and we can take the length of DF columns to get the number of poems when we use Part A it set the data types for all of our fields which is a huge advantage over CSV it's still worth checking especially if you aren't the one defining it here we can use D types on our data frame to create a list of tuples containing a column name and its corresponding data type in this video we learned about the data set we will be using and the problem we will be trying to solve additionally we learned how to check to see if our data loaded properly by checking rows columns and data types now it's your turn to apply what you've learned in the exercises to verify our datawhat's the point of doing an analysis if you aren't solving the right problem in this video we'll define our problem in the context of our data we were going to build a model to predict how much our house sells for this question can be interpreted in multiple ways which is why it's important to take the time to formally define it let's assume we are real estate tycoons looking for the next best investment opportunity for a given house on the market with a listed price and a series of attributes describing the home what is it likely to actually sell for aka the sales closed price the data set we have it is a sample of homes that sold over the course of 2017 using the sample we are to provide a quick proof of concept of whether it's worth investing in more data for the 5.5 million homes that sold in the u.s. in 2017 to do this we need to understand some of the limitations of the data we have first we only have a small geographical area so to apply our model to new areas pose a serious risk we know that we only have residential data so we shouldn't expect to predict how much a business location is worth lastly we only have one year's worth of data which will make it hard to draw strong conclusions about the seasonality in the data set the original data set has hundreds of attributes available but in order to start simple we've already worked with our client to identify around 50 attributes they think are likely to influence the price of a home these attributes generally fall into these groups for dates we have date listed and the year the home was built for a locational data we have the city that the home is in the its school district and it's actual postal address we also have many different metrics to gauge the size of a home like number of bed and bathrooms as well as the area of living space for prices we have the listing price and we would be able to predict anything without the sales price we also have a lot of data available on the amenities that a house has like a pool or garage as well as the construction materials that were used to build the house big data means a lot can go wrong when loading data make sure you have the right number of Records and columns we can use DF count to get the row count DF columns to get the list of columns and we can take the length of DF columns to get the number of poems when we use Part A it set the data types for all of our fields which is a huge advantage over CSV it's still worth checking especially if you aren't the one defining it here we can use D types on our data frame to create a list of tuples containing a column name and its corresponding data type in this video we learned about the data set we will be using and the problem we will be trying to solve additionally we learned how to check to see if our data loaded properly by checking rows columns and data types now it's your turn to apply what you've learned in the exercises to verify our data\n"