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Fix #818: Replace relative image paths with absolute image paths in 2-Regression/3-Linear/README.md

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qrywo 3 months ago
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2-Regression/3-Linear/README.md
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@ -105,11 +105,11 @@ Now that you have an understanding of the math behind linear regression, let's c
From the previous lesson you have probably seen that the average price for different months looks like this: From the previous lesson you have probably seen that the average price for different months looks like this:
<img alt="Average price by month" src="../2-Data/images/barchart.png" width="50%"/> <img alt="Average price by month" src="/2-Regression/2-Data/images/barchart.png" width="50%"/>
This suggests that there should be some correlation, and we can try training linear regression model to predict the relationship between `Month` and `Price`, or between `DayOfYear` and `Price`. Here is the scatter plot that shows the latter relationship: This suggests that there should be some correlation, and we can try training linear regression model to predict the relationship between `Month` and `Price`, or between `DayOfYear` and `Price`. Here is the scatter plot that shows the latter relationship:
<img alt="Scatter plot of Price vs. Day of Year" src="images/scatter-dayofyear.png" width="50%" /> <img alt="Scatter plot of Price vs. Day of Year" src="/2-Regression/3-Linear/images/scatter-dayofyear.png" width="50%" />
Let's see if there is a correlation using the `corr` function: Let's see if there is a correlation using the `corr` function:
@ -128,7 +128,7 @@ for i,var in enumerate(new_pumpkins['Variety'].unique()):
ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var) ax = df.plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)
``` ```
<img alt="Scatter plot of Price vs. Day of Year" src="images/scatter-dayofyear-color.png" width="50%" /> <img alt="Scatter plot of Price vs. Day of Year" src="/2-Regression/3-Linear/images/scatter-dayofyear-color.png" width="50%" />
Our investigation suggests that variety has more effect on the overall price than the actual selling date. We can see this with a bar graph: Our investigation suggests that variety has more effect on the overall price than the actual selling date. We can see this with a bar graph:
@ -136,7 +136,7 @@ Our investigation suggests that variety has more effect on the overall price tha
new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar') new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')
``` ```
<img alt="Bar graph of price vs variety" src="images/price-by-variety.png" width="50%" /> <img alt="Bar graph of price vs variety" src="/2-Regression/3-Linear/images/price-by-variety.png" width="50%" />
Let us focus for the moment only on one pumpkin variety, the 'pie type', and see what effect the date has on the price: Let us focus for the moment only on one pumpkin variety, the 'pie type', and see what effect the date has on the price:
@ -144,7 +144,7 @@ Let us focus for the moment only on one pumpkin variety, the 'pie type', and see
pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE'] pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']
pie_pumpkins.plot.scatter('DayOfYear','Price') pie_pumpkins.plot.scatter('DayOfYear','Price')
``` ```
<img alt="Scatter plot of Price vs. Day of Year" src="images/pie-pumpkins-scatter.png" width="50%" /> <img alt="Scatter plot of Price vs. Day of Year" src="/2-Regression/3-Linear/images/pie-pumpkins-scatter.png" width="50%" />
If we now calculate the correlation between `Price` and `DayOfYear` using `corr` function, we will get something like `-0.27` - which means that training a predictive model makes sense. If we now calculate the correlation between `Price` and `DayOfYear` using `corr` function, we will get something like `-0.27` - which means that training a predictive model makes sense.
@ -219,7 +219,7 @@ plt.scatter(X_test,y_test)
plt.plot(X_test,pred) plt.plot(X_test,pred)
``` ```
<img alt="Linear regression" src="images/linear-results.png" width="50%" /> <img alt="Linear regression" src="/2-Regression/3-Linear/images/linear-results.png" width="50%" />
## Polynomial Regression ## Polynomial Regression
@ -248,7 +248,7 @@ Using `PolynomialFeatures(2)` means that we will include all second-degree polyn
Pipelines can be used in the same manner as the original `LinearRegression` object, i.e. we can `fit` the pipeline, and then use `predict` to get the prediction results. Here is the graph showing test data, and the approximation curve: Pipelines can be used in the same manner as the original `LinearRegression` object, i.e. we can `fit` the pipeline, and then use `predict` to get the prediction results. Here is the graph showing test data, and the approximation curve:
<img alt="Polynomial regression" src="images/poly-results.png" width="50%" /> <img alt="Polynomial regression" src="/2-Regression/3-Linear/images/poly-results.png" width="50%" />
Using Polynomial Regression, we can get slightly lower MSE and higher determination, but not significantly. We need to take into account other features! Using Polynomial Regression, we can get slightly lower MSE and higher determination, but not significantly. We need to take into account other features!
@ -266,7 +266,7 @@ In the ideal world, we want to be able to predict prices for different pumpkin v
Here you can see how average price depends on variety: Here you can see how average price depends on variety:
<img alt="Average price by variety" src="images/price-by-variety.png" width="50%" /> <img alt="Average price by variety" src="/2-Regression/3-Linear/images/price-by-variety.png" width="50%" />
To take variety into account, we first need to convert it to numeric form, or **encode** it. There are several way we can do it: To take variety into account, we first need to convert it to numeric form, or **encode** it. There are several way we can do it:

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