Cocalc Regression Ipynb

Jupyter notebook Linear Regression.ipynb To motivate our example of Linear Regression, we will be looking at the problem of predicitng housing prices based on some given features. This notebook will help to give you a bit of a flavor of what it's like to perform predictive analytics on a given dataset. By the end of this, you should have a good enough understanding of the process behind machine learning and being able to leverage scikit-learn to perform regression analysis. Let's start off by importing some of the required libraries we will need for today... First we will have to import the data.

We will use pandas again to import our CSV file. Let's take a look at the first few records to see what we are working with... In the previous lab, you learned to make scatterplots and compute correlation coefficients. This supplement will show you how to fit lines to data and estimate confidence intervals for a regression. In this lab, as in Lab 8 on Correlation, you will study the correlation between head size (cubic centimeters) and brain weight (g) for a group of women. As usual, import pandas, Numpy and Seaborn.

For regression, we'll also import sub-libraries to plot lines and calculate regression lines and correlation coefficients. Using pandas, import the file brainhead.csv and view the resulting data frame. Make a scatterplot of your data. Don’t plot a regression line. Note: The general syntax for making a scatterplot from a pandas dataframe df without fitting a line to it (or showing histograms of variables) is: sns.lmplot("xvar","yvar",data=df,fit_reg=False). In the previous lab, you learned to make scatterplots and compute correlation coefficients.

This supplement will show you how to fit lines to data and estimate confidence intervals for a regression. In this lab, as in Lab 8 on Correlation, you will study the correlation between head size (cubic centimeters) and brain weight (g) for a group of women. As usual, import pandas, Numpy and Seaborn. For regression, we'll also import sub-libraries to plot lines and calculate regression lines and correlation coefficients. Using pandas, import the file brainhead.csv and view the resulting data frame. Make a scatterplot of your data.

Don’t plot a regression line. Note: The general syntax for making a scatterplot from a pandas dataframe df without fitting a line to it (or showing histograms of variables) is: sns.lmplot("xvar","yvar",data=df,fit_reg=False). This returns all keys and values as python dictionary. Here you can view the data set description using boston.DESCR, which describes each feature in the data set. So MEDV is our target variable, which we need to predict and remaining are our features. Now that we got our data loaded, let's get our data frame ready quickly and work ahead.

In new overall dataframe let’s check if we have any missing values in the data set. Simple Linear Regression Simple linear regression is an approach for predicting a response using a single feature. To find the parameters so that the model best fits the data. The line for which the the error between the predicted values and the observed values is minimum is called the best fit line or the regression line. These errors are also called as residuals. The residuals can be visualized by the vertical lines from the observed data value to the regression line.

In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables. Jupyter notebook 1.RegressionBasic.ipynb The following exercises illustrate different aspects of learning theory by using linear regression in a 2-dimensional space as a working learning model. We won't look at the theory of linear regression; we will just use it as a black box in order to understand some fundamental concepts of the theory of learning. Also, we won't look at the varied mathematical detail of the concepts we are introducing.

So this is a tutorial without mathematics or analysis. Now its important to realise that in data science you will rarely use this kind of simple 2-dimensional modelling. The problem is stylised, and you rarely get simple Gaussian noise like this. In many ways this is a trivial, unrealistic example of learning and statistics. However, the simple nature of the material means we can carefully study the different aspects of learning, and discuss some of the behaviour that theory tells us about. So this is excellent material for a tutorial.

Now the name linear regression is a bit confusing because the model allows non-linear curves to be fit to data. The linear part is in the structure of the model. We are only considering the 1-dimensional case for simplicity, so we estimate yyy in terms of xxx. So a simple so-called quadratic would make an estimate for yyy using the form: a∗1+b∗x+c∗x2a*1 + b*x + c*x^2a∗1+b∗x+c∗x2 where we have a vector of 3 parameters, (a,b,c)(a,b,c)(a,b,c), to learn and the function for... The vector of parameters is called the coefficients for the linear regression. Below we plot four different versions of this function for different values of the coefficients (that is, (a,b,c)(a,b,c)(a,b,c)).

Let us use gradient descent approach to minize the cost function cost function: J(w)=12m∑i=1m(h(x(i))−y(i))2J\left( w \right)=\frac{1}{2m}\sum\limits_{i=1}^{m}{{{\left( {{h}}\left( {{x}^{(i)}} \right)-{{y}^{(i)}} \right)}^{2}}}J(w)=2m1​i=1∑m​(h(x(i))−y(i))2 hypothesis/model：\[{{h}}\left( x \right)={{w}^{T}}X={{w }{0}}{{x}{0}}+{{w }{1}}{{x}{1}}+{{w }{2}}{{x}{2}}+...+{{w }{n}}{{x}{n}}\] in order to vectorize the process of computation, we need to add a column vector (equal to 1) to the dataset calculate the cost function(initial w value is 0). set learning rate - alpha set maximum number iteration In this notebook, we learn how to use scikit-learn to implement simple linear regression.

We download a dataset that is related to fuel consumption and Carbon dioxide emission of cars. Then, we split our data into training and test sets, create a model using training set, Evaluate your model using test set, and finally use model to predict unknown value FUEL CONSUMPTION in CITY(L/100 km) e.g. 9.9 FUEL CONSUMPTION in HWY (L/100 km) e.g. 8.9

FUEL CONSUMPTION COMB (L/100 km) e.g. 9.2 CO2 EMISSIONS (g/km) e.g. 182 --> low --> 0

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