This is an introductory regression problem that uses California housing data from the census. There's a description of the original data herebut we're using a slightly altered dataset that's on github and appears to be mirrored on kaggle.

The problem here is to create a model that will predict the median housing value for a census block group called "district" in the dataset given the other attributes.

The original data is also available from sklearn so I'm going to take advantage of that to get the description and do a double-check of the model. These are convenience holders for strings and other constants so they don't get scattered all over the place. We'll grab the data from github, extract it it's a tgz compressed tarfilethen make a pandas data frame from it. I'll also download the sklearn version. The dataset seems to differ somewhat from the sklearn description.

Is this just a problem of names? It looks like the sklearn values are in some cases calculated values derived from the original. It makes sense that they changed some of the things total number of rooms only makes sense if there is the same number of households in each district, for instancebut it would have been better if they documented the changes they made and why they changed it.

These were removed to allow experimenting with missing data.

The original dataset that was collected for the census had all the values. Which makes up about forty-four percent of all the houses. If you look at the median income plot you can see that it goes from 0 to It turns out that the incomes were re-scaled and limited to the 0.

The median age and value were also capped, possibly affecting our price predictions. Toggle navigation Machine Learning Studies. Introduction This is an introductory regression problem that uses California housing data from the census.

Imports These are the dependencies for this problem. Constants These are convenience holders for strings and other constants so they don't get scattered all over the place. The Data We'll grab the data from github, extract it it's a tgz compressed tarfilethen make a pandas data frame from it.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. Problem Statement The purpose of the project is to predict median house values in Californian districts, given many features from these districts. The project also aims at building a model of housing prices in California using the California census data.

The data has metrics such as the population, median income, median housing price, and so on for each block group in California. This model should learn from the data and be able to predict the median housing price in any district, given all the other metrics. Districts or block groups are the smallest geographical units for which the US Census Bureau publishes sample data a block group typically has a population of to 3, people.

There are 20, districts in the project dataset. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up.

**Machine Learning Project - Part 2 - Predict House Prices in California - CloudxLab**

Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Capstone-Project-California-Housing-Price-Prediction Problem Statement The purpose of the project is to predict median house values in Californian districts, given many features from these districts. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. California Housing Price Prediction.Last time we saw how to do logistic regression on titanic dataset which many professional data scientist would say is the first step towards doing a data science project.

But most of the timesdata scientists are given data which are unknown to them. It is very important to know in depth about the data. So far so goodToday we are going to work on a dataset which consists information about the location of the houseprice and other aspects such as square feet etc.

When we work on these sort of datawe need to see which column is important for us and which is not. Our main aim today is to make a model which can give us a good prediction on the price of the house based on other variables.

We are going to use Linear Regression for this dataset and see if it gives us a good accuracy or not. What is good accuracy? What can we infer from the above describe function? Similarlywe can infer so many things by just looking at the describe function.

Nowwe are going to see some visualization and also going to see how and what can we infer from visualization.

You may wonder why is it important? Here in Indiafor a good locality a builder opts to make houses which are more than 3 bedrooms which attracts the higher middle class and upper class section of the society. As we can see from the visualization 3 bedroom houses are most commonly sold followed by 4 bedroom. So how is it useful? But at which locality? So according to the datasetwe have latitude and longitude on the dataset for each house. We are going to see the common location and how the houses are placed.

We use seabornand we get his beautiful visualization. Joinplot function helps us see the concentration of data and placement of data and can be really useful. Let us see what we can infer from this visualization. For latitude between But when we talk about longitude we can see that concentration is high between Let us start withIf price is getting affecting by living area of the house or not? The plot that we used above is called scatter plotscatter plot helps us to see how our data points are scattered and are usually used for two variables.

From the first figure we can see that more the living areamore the price though data is concentrated towards a particular price zonebut from the figure we can see that the data points seem to be in linear direction. Thanks to scatter plot we can also see some irregularities that the house with the highest square feet was sold for very lessmaybe there is another factor or probably the data must be wrong. The second figure tells us about the location of the houses in terms of longitude and it gives us quite an interesting observation that We can see more factors affecting the price.

## Predicting House Price Using Regression Algorithm for Machine Learning

As we can see from all the above representation that many factors are affecting the prices of the houselike square feet which increases the price of the house and even location influencing the prices of the house. Now that we are familiar with all these representation and can tell our own story let us move and create a model to which would predict the price of the house based upon the other factors such as square feetwater front etc.

We are going to see what is linear regression and how do we do it? In easy words a model in statistics which helps us predicts the future based upon past relationship of variables. So when you see your scatter plot being having data points placed linearly you know regression can help you!

The variable we are predicting is called the criterion variable and is referred to as Y. The variable we are basing our predictions on is called the predictor variable and is referred to as X. When there is only one predictor variable, the prediction method is called Simple Regression. We use train data and test datatrain data to train our machine and test data to see if it has learnt the data well or not.It is an open community that hosts forums and competitions in the wide field of data.

In each Kaggle competition, competitors are given a training data set, which is used to train their models, and a test data set, used to test their models. Kagglers can then submit their predictions to view how well their score e. As a team, we joined the House Prices: Advanced Regression Techniques Kaggle challenge to test our model building and machine learning skills. For this competition, we were tasked with predicting housing prices of residences in Ames, Iowa.

Our training data set included houses i. Our testing set included houses with the same 79 attributes, but sales price was not included as this was our target variable. To view our code split between R and Python and our project presentation slides for this project see our shared GitHub repository.

Our first step was to combine these data sets into a single set both to account for the total missing values and to fully understand all the classes for each categorical variable. That is, there might be missing values or different class types in the test set that are not in the training set. As our response variable, Sale Price, is continuous, we will be utilizing regression models. One assumption of linear regression models is that the error between the observed and expected values i.

Violations of this assumption often stem from a skewed response variable. Machine learning algorithms do not handle missing values very well, so we must obtain an understanding of the missing values in our data to determine the best way to handle them. We find that 34 of the predictor variables have values that are interpreted by R and Python as missing i.

Below we describe examples of some of the ways we treated these missing data. In many instances, what R and Python interpret as a missing value is actually a class of the variable.

The NA class describes houses that do not have a pool, but our coding languages interpret houses of NA class as a missing value instead of a class of the Pool Quality variable.

These three houses likely have a pool, but its quality was not assessed or input into the data set. Our solution was to first calculate mean Pool Area for each class of Pool Quality, then impute the missing Pool Quality classes based on how close that house's Pool Area was to the mean Pool Areas for each Pool Quality class. For example, the first row in the below picture on the left has a Pool Area of square feet. The average Pool Area for houses with Excellent pool quality Ex is about square feet picture on the right.

Therefore, we imputed this house to have a Pool Quality of Excellent. Some variables had a moderate amount of missingness. Intuitively, attributes related to the size of a house are likely important factors regarding the price of the house.

Therefore, dropping these variables seems ill-advised.Intuitively, which of the four houses in the picture do you think is the most expensive? Most people will say the blue one on the right, because it is the biggest and the newest. However, you might have a different answer after reading this blog post and discover a more precise approach to predicting prices. In this blog post, we discuss how we use machine learning techniques to predict house prices. The dataset can be found on Kaggle.

The dataset is divided into the training and test datasets. In total, there are about 2, rows and 79 columns which contain descriptive information on different houses e. Most houses are in the range of k to k; the high end is around k to k with a sparse distribution. Exhibit 3: Overall Quality vs. House Sale Price. Most of the variables in the dataset 51 out of 79 are categorical. They include things like the neighborhood of the house, the overall quality, the house style, etc.

The most predictive variables for the sale price are the quality variables. For example, the overall quality turns out to be the strongest predictor for the sale price.

Quality on particular aspect of the house, like the pool quality, the garage quality, and the basement quality, also show high correlation with the sale price. The numeric variables in the dataset are mostly the area of the house, including the first-floor area, pool area, number of bedrooms, garage area, etc.

Most of the variables show a correlation with the sale price.

### Boston Home Prices Prediction and Evaluation

One challenge of this dataset is the missing data. However, for missing data that are missing at random, we use other variables to impute the value. Dealing with a large number of dirty features is always a challenge. This section focuses on the feature engineering creating and dropping variables and feature transformation dummifying variables, removing skewness etc.

Usually it makes sense to delete features that are highly correlated. In our analysis, we found out that GarageYrBlt year the garage was built and YrBlt year the house was built had a very strong positive correlation of 0.You will use your trained model to predict house sale prices and extend it to a multivariate Linear Regression.

Until now, that was impossible. But with this limited offer you can… got a bit sidetracked there. Complete source code notebook Google Colaboratory :. It contains training data points and 80 features that might help us predict the selling price of a house. Most of the density lies between k and k, but there appears to be a lot of outliers on the pricier side.

The basement area seems like it might have a lot of predictive power for our model. Ok, last one. Of course, this one seems like a much more subjective feature, so it might provide a bit different perspective on the sale price. Everything seems fine for this one, except that when you look to the right things start getting much more nuanced.

All the features we discussed so far appear to be present. Its almost like we knew them from the start…. Linear regression models assume that the relationship between a dependent continuous variable Y and one or more explanatory independent variables X is linear that is, a straight line. Linear regression models can be divided into two main types:. X represents our input data and Y is our prediction. A more complex, multi-variable linear equation might look like this, where w represents the coefficients or weights, our model will try to learn.

Given our Simple Linear Regression equation:. The MSE measures how much the average model predictions vary from the correct values.

The first derivative of MSE is given by:. Now that we have the tests ready, we can implement the loss function:. We will do a little preprocessing to our data using the following formula standardization :. But why? Why would we want to do that? The following chart might help you out:. What the doodle shows us that our old friend — the gradient descent algorithm, might converge find good parameters faster when our training data is scaled.

Shall we?Email: solutions altexsoft. When you give customers advice that can help them save some money, they will pay you back with loyalty, which is priceless.

Interesting fact: Fareboom users started spending twice as much time per session within a month of the release of an airfare price forecasting feature. This tool continues to grow conversion for our partner. Besides travel, price predictions find their application in various scenarios. Commodity traders, investors, construction developers, or energy generators use estimates on future price movements for business purposes. The article describes the steps to build a price prediction solution and implementation examples in four industries.

Price forecasting may be a feature of consumer-facing travel apps, such as Trainline or Hopper, used to increase customer loyalty and engagement. At the same time, other businesses may also use information about future prices. Entrepreneurs may need to define an optimal time to buy a commodity to adjust prices of products or services that require a commodity lumber, coffee, goldor evaluate the investment appeal of fixed assets. Price prediction can be formulated as a regression task.

Regression analysis also lets researchers determine how much these predictors influence a target variable. In regression, a target variable is always numeric. Descriptive analytics. Descriptive analytics rely on statistical methods that include data collection, analysis, interpretation, and presentation of findings.

Descriptive analytics allow for transforming raw observations into knowledge one can understand and share. In short, this analytics type helps to answer the question of what happened? Predictive analytics. Predictive analytics is about analyzing current and historical data to forecast the probability of future events, outcomes, or values in the context of price predictions. Predictive analytics requires numerous statistical techniques, such as data mining identification of patterns in data and machine learning.

The goal of machine learning is to build systems capable of finding patterns in data, learning from it without human intervention and explicit reprogramming. To learn more about a machine learning project structurecheck out our dedicated article. Then the specialists collect, select, prepare, preprocess, and transform this data. Once this stage is completed, the specialists start building predictive models. A model that forecasts prices with the highest accuracy rate will be chosen to power a system or an application.

So, the framework of the price prediction task may look like this:. By the early s, the energy sectors in many countries were fully regulated and monopolized.

Government agencies and local bodies were monitoring the work of utility companies, setting their terms of service, pricing, construction plans, ensuring these companies adhered to safety and environmental standards.

Then a shift towards deregulation began, the main goal of which was to reduce electricity costs and ensure a reliable supply of energy via competition. The power industry started turning into a free market where prices for products and services depend on supply and demand. In other words, the market players trade electricity on exchanges like other commodities.

The participants set their bids and offers while trying to maximize their profits. Deregulation is an ongoing process across markets. Electricity is a special commodity type, so trading it is a tricky task. The demand for electricity and, consequently price, depends on the weather temperature, precipitation, wind power, etc.

Non-storability of electrical energy and continuous shifts in demand lead to electricity price volatility.

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