Abalone Dataset

1: Introduction

About this data
The Abalone Dataset involves predicting the age of abalone given objective measures of individuals.

It is a multi-class classification problem, but can also be framed as a regression. The number of observations for each class is not balanced. There are 4,177 observations with 8 input variables and 1 output variable. The variable names are as follows:

1. Sex (M, F, I).

2. Length.

3. Diameter.

4. Height.

5. Whole weight.

6. Shucked weight.

7. Viscera weight.

8. Shell weight.

9. Rings

2: Data Pre processing

2.1 Loading the required Libraries

import sys
assert sys.version_info >= (3, 5)

# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"

# Common imports
import numpy as np
import os
import pandas as pd

# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)

#ML imports
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score