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Auto-generate Jupyter notebooks from Markdown
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‎notebooks/01-introduction.ipynb‎

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"cells": [
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"---\n",
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{
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{
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"cell_type": "markdown",
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"### Representation of Data in Scikit-learn\n",

‎notebooks/02-regression.ipynb‎

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"cells": [
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"---\n",
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{
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"cell_type": "code",
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"cell_type": "markdown",
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"We can see that we have seven columns in total: 4 continuous (numerical) columns named `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm`, and `body_mass_g`; and 3 discrete (categorical) columns named `species`, `island`, and `sex`. We can also see from a quick inspection of the first 5 samples that we have some missing data in the form of `NaN` values. Missing data is a fairly common occurrence in real-life data, so let's go ahead and remove any rows that contain `NaN` values:"
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"In this scenario we will train a linear regression model using `body_mass_g` as our feature data and `bill_depth_mm` as our label data. We will train our model on a subset of the data by slicing the first 146 samples of our cleaned data. \n",
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{
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"cell_type": "code",
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"execution_count": null,
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},
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{
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"cell_type": "markdown",
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"In this regression example we will create a Linear Regression model that will try to predict `y` values based upon `x` values.\n",
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"Next we’ll define a model, and train it on the pre-processed data. We’ll also inspect the trained model parameters m and c:"
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{
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"Now we can make predictions using our trained model, and calculate the Root Mean Squared Error (RMSE) of our predictions:"
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"Finally, we’ll plot our input data, our linear fit, and our predictions:"
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"Congratulations! We've now created our first machine-learning model of the lesson and we can now make predictions of `bill_depth_mm` for any `body_mass_g` values that we pass into our model.\n",
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"Our RMSE for predictions on all penguin samples is far larger than before, so let's visually inspect the situation:"
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"Oh dear. It looks like our linear regression fits okay for our subset of the penguin data, and a few additional samples, but there appears to be a cluster of points that are poorly predicted by our model. Even if we re-trained our model using all samples it looks unlikely that our model would perform much better due to the two-cluster nature of our dataset.\n",
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"### Exercise: Try to re-implement our univariate regression model using these new train/test sets.\n",
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"**Quick follow-up**: Interpret the results of your model. Is it accurate? What does it say about the relationship between body mass and bill depth? Is this a \"good\" model?\n",
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"::::::::::::::::::::::::::::::::::::: callout\n",
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"We can now make predictions on train/test sets, and calculate RMSE"
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"Finally, let's visualise our model fit on our training data and full dataset."
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"::::::::::::::::::::::::::::::::::::: challenge\n",
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"Let's try a model that includes penguin species as a predictor."
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"Since the species column is coded as a string, we need to convert it into a numerical format before we can use it in a machine learning model. To do this, we apply dummy coding (also called one-hot encoding), which creates new binary columns for each species category (e.g., species_Adelie, species_Chinstrap, species_Gentoo). Each row gets a 1 in the column that matches its species and 0 in the others.\n",
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"We can than train/fit and evaluate our model as usual."
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"{% include links.md %}\n",

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