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Prediction and Decision Making

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Analyse des données avec Python

4.6 (4,744 notes) | 45K étudiants inscrits

Cours 6 de 9 dans Certificat professionnel en science des données IBM Spécialisation

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more! Topics covered: 1) Importing Datasets 2) Cleaning the Data 3) Data frame manipulation 4) Summarizing the Data 5) Building machine learning Regression models 6) Building data pipelines Data Analysis with Python will be delivered through lecture, lab, and assignments. It includes following parts: Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. LIMITED TIME OFFER: Subscription is only $39 USD per month for access to graded materials and a certificate.

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Compétences que vous apprendrez

Predictive Modelling, Python Programming, Data Analysis, Data Visualization (DataViz), Model Selection

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4.6 (4,744 notes)

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KA

Apr 22, 2019

I am highly impressed with this course !!\n\nThis course is very informative. Regular assignments helped me a lot to learn and to complete my course on time before the deadline.

TA

Feb 10, 2019

Really this course shows the full path to master the Data Analysis with Python. This path is short, helpful, and rich information their. thank you at all and thank you Coursera

À partir de la leçon

Model Development

Model Development1:44

Linear Regression and Multiple Linear Regression6:34

Model Evaluation using Visualization4:44

Polynomial Regression and Pipelines4:25

Measures for In-Sample Evaluation3:36

Prediction and Decision Making5:03

Enseigné par

Joseph Santarcangelo
Ph.D., Data Scientist at IBM

Transcription

In this video, our final topic will be on prediction and decision making.

How can we determine if our model is correct?

The first thing you should do is make sure your model results make sense.

You should always use visualization,

numerical measures for evaluation and comparing between different models.

Let's look at an example of prediction.

If you recall, we train the model using the fit method.

Now we want to find out what the price would be for

a car that has a highway miles per gallon of 30.

Plugging this value into the predict method gives us a resulting price of $13,771.30.

This seems to make sense.

For example; The value is nauts negative,

extremely high or extremely low.

We can look at the coefficients by examining the coef_attribute.

If you recall the expression for

the simple linear model that predicts price from highway miles per gallon.

This value corresponds to the multiple of the highway miles per gallon feature,

as such an increase of one unit in highway miles per gallon.

The value of the car decreases approximately $821.

This value also seems reasonable.

Sometimes your model will produce values that don't make sense.

For example, if we plot the model out for highway miles per

gallon in the ranges of 0 to 100 we get negative values for the price.

This could be because the values in that range are not realistic.

The linear assumption is incorrect or we don't have data for cars in that range.

In this case, it is unlikely that a car will have fuel mileage in that range.

So our model seems valid.

To generate a sequence of values in a specified range,

import numpy, then use the numpy arrange function to generate the sequence.

The sequence starts at one and increments by one till we reach 100.

The first parameter is the starting point of the sequence.

The second parameter is the endpoint plus one of the sequence.

The final parameter is the step size between elements in the sequence.

In this case, it's one.

So we increment the sequence one step at a time.

From one to two and so on.

We can use the output to predict new values.

The output is a numpy array,

many of the values are negative.

Using a regression plot to visualize your data is the first method you should try.

See the labs for examples of how to plot polynomial regression.

For this example, the effect of the independent variable is evident in this case.

The data trends down as the dependent variable increases.

The plot also shows some nonlinear behavior.

Examining the residual plot,

we see in this case the residuals have a curvature suggesting nonlinear behavior.

A distribution plot is a good method for multiple linear regression.

For example, we see the predictive values for prices in

the range from 30,000 to 50,000 are inaccurate.

This suggests a non-linear model may be more suitable or we need more data in this range.

The mean square error is perhaps

the most intuitive numerical measure for determining if a model is good or not.

Let's see how different measures of mean square error impact the model.

The figure shows an example of a mean square error of 3,495.

This example has a mean square error of 3,652.

The final plot has a mean square error of 12,870.

As the square error increases,

the targets get further from the predicted points.

As we discussed, R-squared is another popular method to evaluate your model.

In this plot, we see the target points in red and

the predicted line in blue and R-squared of 0.9986.

The model appears to be a good fit.

This model has an R-squared of 0.9226.

There still is a strong linear relationship in R- squared of 0.0806,

the data is a lot more messy but the linear relation is evident.

In R-squared 0.61, the linear function is harder to see but on closer inspection we see

the data is increasing with

the independent variable and acceptable value

for R-squared depends on what field you're studying.

Some authors suggest a value should be equal to or greater than 0.10.

Comparing MLR and SLR is a lower MSE always implying a better fit?

Not necessarily.

MSE for an MLR model will be smaller than the MSE for an SLR model,

since the errors of the data will decrease when more variables are included in the model.

Polynomial regression will also have a smaller MSE than regular regression.

A similar inverse relationship holds for R-squared.

In the next section we'll look at better ways to evaluate the model.

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