Day 22 - Seaborn - Line Plot, Barplot

-

    By Jerin Lalichan 


Line Plot

    The line plot is one of the most basic plots in the seaborn library. This plot is mainly used to visualize the data in the form of some time series, i.e. in a continuous manner.

 



Barplot

    A barplot is basically used to aggregate the categorical data according to some methods and by default, it’s the mean. It can also be understood as a visualization of the group by action. To use this plot we choose a categorical column for the x-axis and a numerical column for the y-axis, and we see that it creates a plot taking a mean per categorical column.














   
  I am doing a challenge - #66DaysofData  in which I will be learning something new from the Data Science field for 66 days, and I will be posting daily topics on my LinkedIn, On my GitHub repository, and on my blog as well.


Stay Curious!  

Comments

Post a Comment

Popular posts from this blog

Day 17 - Ensemble Techniques in ML - Averaging, Weighted average

-
   By  Jerin Lalichan  Ensemble Techniques in ML      Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model.      Ensemble methods include building multiple models and combining them to achieve better outcomes. To put it another way, they integrate the conclusions drawn from various models to enhance overall performance. Generally speaking, ensemble methods produce more accurate results than a single model would.     F or example, let's consider the case in which you need to decide if you should go to a particular movie or not. You can infer that a diverse group of people are likely to make better decisions as compared to individuals. So it's best to check online reviews since it is an aggregation of reviews of hundreds of people from different backgrounds when compared to asking a few of your friends.            Similar is true for a diverse set of models in comparison to single models. This diversif
Read more

Day 4 - Performance metrics in Machine Learning - Regression

-
Image
      By  Jerin Lalichan  Performance metrics in ML     Evaluation of the performance of a model is important. Performance metrics are certain measures to quantify the performance of the model during the training and testing phases. In Machine learning, there are generally two kinds of performance metrics in use. For regression models and for classification models. Below are the most popular metrics in use: Regression Metrics Mean Squared Error (MSE) It is simply the average of the sum of the squares of the difference between the actual value and predicted values. Due to the squaring in this equation, small errors are overestimated. Also because for that reason, this is very much prone to outliers.  2 .  Mean Absolute Error (MAE) Mean Absolute Error is the average of the difference between the ground truth(actual value)  and the predicted values . Since there is no squaring, the error estimated is not exaggerated or overestimated. Also, it is better with outliers than MSE. Even though
Read more