This allows you to decide the level of clustering that is most suitable. Summary In this chapter, you understood the meaning of machine learning and its different types. You were introduced to commonly used machine learning algorithms as well. In the next chapter, you'll learn how to create linear regression models. It's widely used in various industries to create models, which will help in a business.
For example, in the retail industry, there are various factors affecting the sale of a product. These factors could be the price, promotions, or seasonal factors, to name a few.
A linear regression model helps in understanding the influence of each of these factors on the sales of a product as well as to calculate the baseline sales, which is basically the number of sales of this product in the event that there were no external factors, such as price, promotions, and so on. In the preceding chapter, you were introduced to linear regression along with an example of a simple linear regression.
If the correlation value is negative, then the relation between the two variables is strong, but is in the opposite direction. We then use the fit method of lm to define the dependent and independent variable, where in our case, the weight is the dependent variable and the height is the independent variable. To get the intercept value, we use lm. The last line of the code helps in creating a DataFrame of the independent variable and its corresponding coefficients.
This will be useful when we explore multiple regression in detail. The following observations can be made: 1. The average height of a basketball player is around 6. The shortest player is 5. The tallest player is 7.
The player with the least weight is at pounds, which is quite obscure. The heaviest player is pounds. The highest score scored per game by a player is The least scored is On an average, the players score 12 points.
There is a high correlation between height and weight. There is a weak positive correlation between successful field goals in terms of height and weight. The distribution looks quite random. We can also see that the players who are almost pounds have the maximum variations in terms of score, so a hypothesis can be made that the taller and heavier players have a greater score, while the shorter and heavier players have a lower score.
The overall distribution here is also quite scattered. From the preceding analysis of the correlation and distribution, we can see that there are no clear-cut patterns between the average points scored and the independent variables. It can be expected that the model that will be built with the existing data won't be highly accurate. We'll learn how to build the linear regression models using the following packages: The statsmodels module The SciKit package Even pandas provides an Ordinary Least Square OLS regression, which you can experiment with after you've completed this chapter.
The ordinary least square is a method to estimate unknown coefficients and intercepts for a regression equation. We'll start off the with the statsmodels package.
The statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. The fit method helps in fitting the model. The following image shows the summary of the regression model that we trained earlier, which shows the various metrics associated with the model: The preceding summary gives quite a lot of information about the model.
The main parameter to look for is the r square value, which tells you how much of the variance of the dependent variable is captured by the model. It ranges from 0 to 1, and the p value tells us if the model is significant. From the preceding output, we can see that the R-square value is 0. As a rule of thumb, any p value of a variable less than 0. The preceding model can be iterated multiple times with the different combination of variables till the best model is arrived at.
Let's apply both the models on the test data and see how the mean squared error between the actual and the predicted value is. To make a highly accurate model, we need some more variables, which have an influence on the average points that are scored. The preceding model was constructed using the statsmodels package.
We'll now build a model using SciKit. The highest value is of relevance and you can see that it is similar to the one we built with the statsmodels. We then created regression models using the statsmodels and SciKit package. In the next chapter, we'll learn how to perform the probability scoring of an event that takes place using logistic regression. It is used as a classification technique with a binary outcome. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory predictor variables, using a logistic function.
In this chapter, you'll learn to: Build a logistic regression model with statsmodels Build a logistic regression model with SciKit Evaluate and test the model Logistic regression We'll use the Titanic dataset, which was utilized in Chapter 3, Finding a Needle in a Haystack, to help us build the logistic regression model. Since we have already explored the data, we won't be performing any exploratory data analysis as we already have a context for this data.
Also, we'll remove the rows with the missing values. We'll use a Python package called Patsy, which helps in describing statistical models. It helps in defining a dependent and independent variable formula that is similar to R. The variables enclosed within C are treated as categorical variables.
After this, we take the first rows as the training set and the remaining rows in the df DataFrame as the test set. Finally, we use the dmatrices of the Patsy package, which takes in the formula and input a DataFrame to create a DataFrame. This is ready to be inputted to the modeling functions of statsmodels and SciKit. The pseudo r square is similar to the r square of linear regression, which is used to measure the goodness of it.
A pseudo r square value between 0. This helps us to understand which areas of the predicted probability are denser. From the preceding plot, we can see that the density is higher near the probabilities of 0 and 1, which is a good sign and shows that the model is able to predict some patterns from the data given.
It also shows that the density is the highest near 0, which means that a lot of people did not survive. This proves the analysis we performed in Chapter 3, Finding a Needle in a Haystack. We can see that the model prediction shows that if the passenger is male, then the chances of survival are lower compared to females.
This was also shown in our analysis in Chapter 3, Finding a Needle in a Haystack, where it was seen that females had a higher survival rate. For the remaining passengers, there is a more or less random distribution. Evaluating a model based on test data Let's predict by using the model on the test data and also show the performance of the model through precision and recall by maintaining a threshold of 0. We use the crosstab function of pandas, which helps in displaying the frequency distribution between two variables.
Note that the precision and recall values will vary with the threshold that is used. Let's understand what precision and recall mean. Precision: Precision tells you that among all the predictions of class 0 or class 1, how many of them have been correctly predicted. Recall: Recall tells you that out of the actual instances, how many of them have been predicted correctly.
Here are some of our observations: False Positive FP : This is a positive prediction, which is actually wrong. So, in the preceding cross tab, 21 is False Negative So, a False Positive rate tells us that among all the people who did not survive, what percentage have been predicted as survived.
The True Positive rate tells us that among all the people who survived, what percentage of them have been predicted as survived. Ideally, False Positive rates should be low and True Positive rates should be high. An area of 1 represents a perfect test; an area of 0.
A rough guide to classify the accuracy of a diagnostic test is the traditional academic point system as follows: Range Category This refers to excellent A This refers to good B This refers to fair C This refers to poor D This refers to fail F [ ].
Our model gives us an AUC of 0. We can see that the coefficients of our predictor are similar but not same as the model built using the statsmodels package. There are two instances of positive predictions that have shifted to negative predictions. You learned how to build a logistic regression model using statsmodels and SciKit, and then how to evaluate the model and see whether it's a good model or not.
In the next chapter, you'll learn how to generate recommendations, such as the ones you see on where you'll be recommended new items based on your purchase history. Similar items can also be shown to you based on the product that you are currently browsing. Collaborative filtering methods have been applied to many different kinds of data, including sensing and monitoring data, such as mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data and so on.
The basic principle behind the collaborative filtering approach is that it tries to find people who are similar to each other by looking at their tastes. Let's say if a person primarily likes action movies, then it will try to find a person who has seen similar kinds of movies and it will try to recommend the one that hasn't been seen by the first person, but seen by the second person.
We'll be focusing on the following types of collaborative filtering in this chapter: User-based collaborative filtering Item-based collaborative filtering [ ]. Finding similar users When you have data about what people like, you need a way to determine the similarity between different users. The similarity between different users is determined by comparing each user with every other user and computing a similarity score. This similarity score can be computed using the Pearson correlation, the Euclidean distance, the Manhattan distance, and so on.
The Euclidean distance score The Euclidean distance is the minimum distance between two points in space. Let's try to understand this by plotting the users who have watched Django Unchained and Avengers. As seen in the preceding code, the smaller the Euclidean distance, the greater is the similarity.
We'll divide the Euclidean distance by 1 so that we get a metric that represents a greater similarity for a higher number.
We'll also add 1 in the denominator to avoid getting ZeroDivisionError. The Euclidean distance is how far apart the users are from each other, whereas the Pearson correlation takes into account the association between two people. We'll use the Pearson correlation to compute the similarity score between two users.
I would like to know the people who are most similar to me. The following image shows how to compute a score for the movies so that we can find out what the most recommended movie is: [ ]. We then sum up this new score and then divide it by the applicable similarity score.
In summary, we are taking the weighted average based on the similarity score. From the preceding output, we can see that Gone Girl has a very good score in terms of being recommended, and this is then followed by Kill the Messenger.
Item-based collaborative filtering User-based collaborative filtering finds the similarities between users, and then using these similarities between users, a recommendation is made. Item-based collaborative filtering finds the similarities between items. This is then used to find new recommendations for a user. The following table shows the movies seen by Toby under the Movie column and the rating given by Toby.
The Movie column contains movies similar to the ones seen by Toby. The columns with R as a prefix are the products of the rating and similarity score. Finally, we normalize the values by summing the R prefixed column, then dividing it by the sum of the similarity score of the Movie column.
The following table shows Kill The Messenger as the most recommended movie: [ ]. Skip to main content. Start your free trial. Book Description Explore the world of data science through Python and learn how to make sense of data About This Book Master data science methods using Python and its libraries Create data visualizations and mine for patterns Advanced techniques for the four fundamentals of Data Science with Python - data mining, data analysis, data visualization, and machine learning Who This Book Is For If you are a Python developer who wants to master the world of data science then this book is for you.
What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables.
General Knowledge and Current Affairs. Dispelling Inductor Myths. Computer Languages. User defined function Python part 2. Soft Skills. India Darshan. Louvre Museum. Popular Posts. Hypothesis Space :- The space of all hypothesis that can, in principle, be output by a learning algorithm.
O'Reilly is a leader in ebooks for data analysis and visualization. Um Ihnen ein besseres Nutzererlebnis zu bieten, verwenden wir Cookies. What You Will Learn Manage data and perform linear algebra in Python Derive inferences from the analysis by performing inferential statistics Solve data science problems in Python Create high-end visualizations using Python Evaluate and apply the linear regression technique to estimate the relationships among variables.
Build recommendation engines with the various collaborative filtering algorithms Apply the ensemble methods to improve your predictions Work with big data technologies to handle data at scale In Detail Data science is a relatively new knowledge domain which is used by various organizations to make data driven decisions.
Style and approach This book is an easy-to-follow, comprehensive guide on data science using Python. Related Categories. Related Authors. Free access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support Downloading the example code Downloading the color images of this book Errata Piracy Questions 1.
Finding a Needle in a Haystack What is data mining? Presenting an analysis Studying the Titanic Which passenger class has the maximum number of survivors?
What was the survival percentage among different age groups? Summary 4. Making Sense of Data through Advanced Visualization Controlling the line properties of a chart Using keyword arguments Using the setter methods Using the setp command Creating multiple plots Playing with text Styling your plots Box plots Heatmaps Scatter plots with histograms A scatter plot matrix Area plots Bubble charts Hexagon bin plots Trellis plots A 3D plot of a surface Summary 5.
Uncovering Machine Learning Different types of machine learning Supervised learning Unsupervised learning Reinforcement learning Decision trees Linear regression Logistic regression The naive Bayes classifier The k-means clustering Hierarchical clustering Summary 6. Estimating the Likelihood of Events Logistic regression Data preparation Creating training and testing sets Building a model Model evaluation Evaluating a model based on test data Model building and evaluation with SciKit Summary 8.
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Z-test vs T-test. The F distribution. The chi-square distribution. The chi-square test of independence. Presenting an analysis. Studying the Titanic. Controlling the line properties of a chart.
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Next, you will apply the linear regression technique and also learn to mastering python for data science pdf free download the logistic regression technique to your applications, before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you will perform K-means clustering, along with an analysis of unstructured data with different text mining techniques and leveraging the power of Python in big data analytics. This book is an easy-to-follow, comprehensive guide on data science using Python. The topics covered in the book can all be used in real world scenarios. Skip to main content. data analysis, data visualization, and machine learningWho This Book Is ForIf you are a Python developer who wants to master the world of data science then. onoroff.biz ISBN: | pages | 7 Mb Download Mastering Download Mastering Python Data Analysis for iphone, kindle, reader for free. 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