# Abalone Age Prediction Using Python Jupyter Notebook | Sample Paper

**ABSTRACT:**

Abalones, also known as ear shells or sea ears, are sea snails or mollusks. Because of the economic value of abalone age and the time-consuming method of measuring it, much research has been done to solve abalone age estimation using physical measurements from the UCI dataset.

**1) INTRODUCTION:**

Abalones are endangered marine snails that live in cold coastal waters around the world, with populations mostly found off the coasts of New Zealand, South Africa, Australia, Western North America, and Japan. They are widely consumed in Latin America, France, New Zealand, Southeast Asia, China, Vietnam, Japan, and Korea, and are considered a delicacy and highly nutritious food. They are also commercially farmed for mother-of-pearl production. Because of their iridescence, abalone shells are used as decorative items. As a result, abalone is a widely sought-after product with considerable economic value.

**2) The Abalone Dataset:**

The Abalone Dataset was originally published at the UCI Machine Learning Repository and the original problem was to calculate the age of abalone by counting the number of rings in their shell. Counting the number of rings in an abalone shell, on the other hand, is an expensive process. As a result, estimating the number of rings on an abalone using measurements like height, diameter, length, and weight is a viable option.

**3) DATASET ANALYSIS**

Each of these characteristics, as well as their relationships, were examined in this report.

**LITERATURE REVIEW:**

The first publication of the abalone dataset was in 1995. Since then, extensive research has been conducted using a variety of algorithms and methods, the first of which is decision trees. CLOUDS, a decision tree-based algorithm, achieved a 26.4 percent accuracy rate on the abalone research dataset in 1999.

The algorithm for choosing a split point for the dataset at each internal cluster node typically involves the sorting of the values of every numbering attribute, calculating the Gini index for each possible split point and selecting the split point at the lowest Gini value (the evaluation metric for decision tree classification). It has been found that this approach is costly and difficult computationally.

**From the below visualization we can get to know about : **

The distribution of each attribute is examined individually in this section. The distribution of the goal attribute Rings is the first thing we look at. The remaining points are divided into three classes for ease of analysis: a Size group containing attributes that reflect abalone proportions, a Weight group containing the various weight attributes, and a third group containing only the Sex attribute. Histograms and boxplots were used to evaluate continuous or quantitative attributes, while bar plots were used to analyze categorical features.

According to the study, the Ring attribute values vary from 1 to 29 rings on an abalone specimen. The most common values of Rings, on the other hand, are highly clustered around the distribution's median, so the 2nd and 3rd quartiles are classified in a range of less than one standard deviation. We note that a normal curve can approximate the distribution of this attribute.

**SizeAttributes**

In this part, we'll look at the attributes that describe an abalone's dimensions. Length, Diameter, and Height are the three attributes. We produced two histograms and a boxplot for each of these characteristics. The first histogram is a density histogram with a kernel density estimate, and the second is the attribute's absolute frequencies with ticks and bins adjusted. We see an approximately normal distribution once more. The Height histogram, on the other hand, forms a high peak.

Thus, we filter these outliers in order to obtain a more realistic visualization of the distribution of the Height attribute:

The Sex attribute is a categorical variable with the following possible values: M (Male), F (Female), and I (Infant) (an abalone which is not adult). We used a bar plot to examine the count of each group and concluded that the dataset is balanced in this regard.

**MultivariateAnalysis**

We looked at how the dataset attributes are linked and how the independent variables affect the target variable in this section. The correlation matrix was visualized in a heatmap as the first step in the multivariate analysis:

When we look at the correlation matrix, we can see that the attributes that most strongly correlate with Rings are Height and Shell Weight. As a result, we focused our multivariate analysis on the relationship between these two attributes and Rings:

We observe a curious pattern: we have concentrated values of Height and Shell weight for lower values of Rings. The scatterplot becomes larger as the value of Rings increases, and it becomes dispersed for the highest values of Rings.

**How correlation varies with the number of rings?**

We agreed to look into the variance of the association in terms of the number of rings further after the previous study. We evaluated a variety of values and discovered that the area delimited by Rings 10 has a higher correlation between the independent and target variables.

The violin plots below indicate that as instances are clustered by Rings, the median of Size attributes increases:

When we compare Height and Shell Weight to Rings, we get a similar result:

**Influence of Sex on attributes**

Finally, we look at how the gender classification affects the distribution of the variables Rings, Height, and Shell weight. Our goal is to see if different abalone groups have different distribution parameters or even different types.

The first move is to examine how various Sex groups influence the number of Rings. The median Rings for the I group is smaller than the M and F categories' medians.

Finally, we look at how the groups affect the ring-to-height and ring-to-shell-weight correlations. We've already established that these characteristics have a stronger association with lower Ring value

Since infant abalones have lower ring values, their height and shell weight have a better relationship with rings. When we look at the regression curve for the Infant group, we can see that it has a more 45-degree tendency.

**Conclusion And Future Work:**

We may construct a model to predict the target value in function of the independent attributes by looking at the correlation between the target attribute Rings and the independent variables.

Abalones vary in weight about their size.

There are no significant variations in height, weight, or several rings between male and female abalones.

The Infant Abalones have smaller, lighter, and fewer rings than the other types.

The scatter plots for individual folds of cross-validations show that the Mean Absolute Error can be reduced to less than one for such data arrangements, with the best value being 0.936. However, it also reveals that in certain areas, the error is still above reasonable limits. Given an appropriate and balanced dataset, the author believes that RANSAC, along with SMOTE and Cross-Validation, will achieve the target of a Mean Absolute Error of less than 0.5 across all labels.

**Abalones' weight and height differ with age before they reach adulthood; after that, size and weight cease to vary, and after 16.5 years (15 rings), these measurements are no longer associated.**

**References**

1) https://paperswithcode.com/paper/balancing-competing-objectives-with-noisy

2) https://www.programmersought.com/article/32755557754/

4) https://towardsdatascience.com/a-beginners-guide-to-data-analysis-in-python-188706df5447

5) https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/

6) https://www.fullstackpython.com/data-analysis.html

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