Contents:

· Abalone

· About dataset

· Project report

· Notebook code

· Flask code

· Frontend code (html bootstrap)

What is Abalone?

Abalone is a common name for a group of small to very large sea snails, marine gastropod mollusks in the family Haliotidae. These snails have a large, flattened, ear-shaped shell with a row of holes along the outer edge. The inner surface of the shell is iridescent and is highly prized for its beauty and used in jewelry and decorative items.

Abalone is also a popular food source in many cultures, particularly in Asia and North America. The meat is considered a delicacy and is often used in sushi, salads, and other dishes. Because of its popularity as a food and the high demand for its shells, many species of abalone have been overfished and are now endangered.

About Dataset:

The Abalone dataset is a popular machine learning dataset that contains measurements of physical characteristics of abalone, a type of sea snail. The dataset is often used as a benchmark for regression tasks in machine learning.

The dataset includes the following features or variables for each abalone:

Sex: categorical variable (M for male, F for female, and I for infant)

Length: continuous variable representing the longest shell measurement in mm

Diameter: continuous variable representing the diameter of the shell in mm

Height: continuous variable representing the height of the shell in mm

Whole weight: continuous variable representing the weight of the whole abalone in grams

Shucked weight: continuous variable representing the weight of the meat in grams

Viscera weight: continuous variable representing the weight of the gut (after bleeding) in grams

Shell weight: continuous variable representing the weight of the shell in grams

Rings: integer variable representing the age of the abalone (the number of rings on the shell)

The goal of the dataset is to predict the age of the abalone (i.e., the number of rings) based on its physical characteristics. This is a regression task, as the target variable (age) is a continuous variable.

The dataset contains 4,177 instances and has been preprocessed to remove any missing values and to transform the categorical variable (sex) into a set of binary variables (one-hot encoding).

Project Report:

1. Introduction

Problem Statement

The objective of this project is to develop a machine learning model to predict the age of abalone based on its physical characteristics. The model will be trained using the Abalone dataset and deployed on a website using Flask and HTML.

Abalone Dataset

The Abalone dataset is a popular machine learning dataset that contains measurements of physical characteristics of abalone, a type of sea snail. The dataset is often used as a benchmark for regression tasks in machine learning.

Objectives

The objectives of this project are:

To preprocess and analyze the Abalone dataset

To develop a machine learning model to predict the age of abalone

To deploy the model on a website using Flask and HTML

2. Data Preprocessing

Loading and Exploring the Dataset

The first step in any machine learning project is to load and explore the dataset. In this project, we will load the Abalone dataset using Python’s pandas library and explore its features using descriptive statistics.

Data Cleaning and Handling Missing Values

After exploring the dataset, we will check for any missing values and handle them appropriately. We will also check for any outliers or anomalies in the data and remove or correct them as necessary.

Feature Selection and Transformation

Once the dataset is cleaned, we will select the relevant features for our model and transform them as necessary. This may involve converting categorical variables into numerical variables, scaling the data, or applying other transformations.

Data Visualization to Gain Insights

We will use various data visualization techniques to gain insights into the dataset and understand the relationships between the different features. This will help us select the appropriate machine learning algorithm for our model.

3. Model Development

Splitting the Dataset into Training and Testing Sets

Before developing the machine learning model, we will split the dataset into training and testing sets. The training set will be used to train the model, while the testing set will be used to evaluate its performance.

Selection of a Suitable Machine Learning Algorithm

There are many machine learning algorithms that can be used for regression tasks such as this. We will evaluate the performance of several algorithms and select the one that gives the best results.

Hyperparameter Tuning and Cross-Validation

Once we have selected the machine learning algorithm, we will tune its hyperparameters to optimize its performance. We will also use cross-validation to ensure that our model is not overfitting to the training data.

Model Evaluation and Selection of Metrics

After training the model, we will evaluate its performance using appropriate metrics such as mean squared error or mean absolute error. We will also visualize the results to gain insights into the model’s performance.

4. Model Deployment on a Website

Building a Flask Web Application

To deploy the machine learning model on a website, we will use the Flask web framework. We will build a simple web application that allows users to enter the physical characteristics of an abalone and get a prediction of its age.

Creating an HTML Front-End

We will create an HTML front-end for our web application using Bootstrap and JavaScript. The front-end will provide a user-friendly interface for entering data and displaying the results.

5. Conclusion

Summary of Results

In this project, we developed a machine learning model to predict the age of abalone based on its physical characteristics. We preprocessed and analyzed the Abalone dataset, selected a suitable machine learning algorithm, and optimized its hyperparameters using cross-validation. We deployed the model on a website using Flask and HTML, providing a user-friendly interface for users to interact with the model.

Note Book Code:

import numpy as n

import pandas as pd[63]abalone = pd.read_csv("abalone.csv")[64]abalone.head()Ask Six Questions Before moving forward[65]abalone.shape(4177, 9)[66]abalone.info()RangeIndex: 4177 entries, 0 to 4176Data columns (total 9 columns): #  Column         Non-Null Count Dtype --- ------         -------------- -----  0  Sex            4177 non-null  object 1  Length         4177 non-null  float64 2  Diameter       4177 non-null  float64 3  Height         4177 non-null  float64 4  Whole weight   4177 non-null  float64 5  Shucked weight 4177 non-null  float64 6  Viscera weight 4177 non-null  float64 7  Shell weight   4177 non-null  float64 8  Rings          4177 non-null  int64 dtypes: float64(7), int64(1), object(1)memory usage: 293.8+ KB[67]abalone.isnull().sum()Sex              0Length           0Diameter         0Height           0Whole weight     0Shucked weight   0Viscera weight   0Shell weight     0Rings            0dtype: int64[68]abalone.duplicated().sum()0[69]abalone.describe()Encoding[70]abalone['Sex'].value_counts()M   1528I   1342F   1307Name: Sex, dtype: int64[71]abalone['Sex'] = abalone['Sex'].map({"M":0,"F":1,"I":2})[72]abalone['Sex'].value_counts()0   15282   13421   1307Name: Sex, dtype: int64EDA ( Exploratory data Analysis)[76]corr = abalone.corr()[77]import seaborn as snssns.heatmap(corr,annot=True,cbar=True,cmap='coolwarm')Distribution of target variable (age)[78]sns.histplot(abalone['Rings'],bins=20)[80]abalone['Rings'].value_counts()9    68910   6348    56711   4877    39112   2676    25813   20314   1265    11515   10316    6717    584     5718    4219    3220    263     1521    1423     922     627     224     21      126     129     12      125     1Name: Rings, dtype: int64Scatter plot of length vs age[82]sns.scatterplot(x='Length',y='Rings',data=abalone)Train Test Split[83]X = abalone.drop('Rings',axis=1)y = abalone['Rings'][84]from sklearn.model_selection import train_test_split[85]X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)Standarization the data[86]from sklearn.preprocessing import StandardScalersc = StandardScaler()X_train_scaled = sc.fit_transform(X_train)X_test_scaled = sc.transform(X_test)[87]X_test_scaledarray([[-1.16554476, 0.54762541, 0.5216659 , ..., -0.14814366,       -0.29539849, 0.69031287],      [-1.16554476, -1.80260763, -1.65723934, ..., -1.38934271,       -1.34175878, -1.37480684],      [ 1.25995026, -1.29898627, -1.30253383, ..., -1.21751916,       -1.15441916, -1.21650498],      ...,      [ 1.25995026, -0.92127024, -0.94782833, ..., -0.82865534,       -0.92595622, -1.0366165 ],      [-1.16554476, 0.84140454, 0.77502697, ..., 1.02070864,        1.02511735, 1.69768834],      [-1.16554476, -1.29898627, -1.2011894 , ..., -1.12934655,       -1.24123508, -1.22370052]])[88]X_test_scaledarray([[-1.16554476, 0.54762541, 0.5216659 , ..., -0.14814366,       -0.29539849, 0.69031287],      [-1.16554476, -1.80260763, -1.65723934, ..., -1.38934271,       -1.34175878, -1.37480684],      [ 1.25995026, -1.29898627, -1.30253383, ..., -1.21751916,       -1.15441916, -1.21650498],      ...,      [ 1.25995026, -0.92127024, -0.94782833, ..., -0.82865534,       -0.92595622, -1.0366165 ],      [-1.16554476, 0.84140454, 0.77502697, ..., 1.02070864,        1.02511735, 1.69768834],      [-1.16554476, -1.29898627, -1.2011894 , ..., -1.12934655,       -1.24123508, -1.22370052]])Training Models[89]from sklearn.linear_model import LinearRegression, Ridge, Lassofrom sklearn.tree import DecisionTreeRegressorfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.metrics import mean_squared_error, r2_score[90]# Define a list of models to train and comparemodels = [    ('Linear Regression', LinearRegression()),    ('Ridge Regression', Ridge()),    ('Lasso Regression', Lasso()),    ('Decision Tree', DecisionTreeRegressor(random_state=42)),    ('Random Forest', RandomForestRegressor(random_state=42))]# Train and evaluate each modelfor name, model in models:    model.fit(X_train, y_train)    y_pred = model.predict(X_test)    mse = mean_squared_error(y_test, y_pred)    r2 = r2_score(y_test, y_pred)    print(f'{name}: MSE = {mse:.2f}, R2 = {r2:.2f}')Linear Regression: MSE = 4.96, R2 = 0.56Ridge Regression: MSE = 5.07, R2 = 0.56Lasso Regression: MSE = 11.41, R2 = -0.00Decision Tree: MSE = 9.19, R2 = 0.19Random Forest: MSE = 4.99, R2 = 0.56# The MSE represents the average squared difference between the predicted and actual values, and a lower MSE indicates better performance.# The R2 score represents the proportion of variance in the target variable that is predictable from the independent variables, and a higher R2 score indicates better performance.Chosen Model[95]dtr = DecisionTreeRegressor()dtr.fit(X_train, y_train)y_pred = dtr.predict(X_test)print(mean_squared_error(y_test, y_pred))print(r2_score(y_test,y_pred))9.0263473053892210.2089537099162797Prediction System[101]def prediction_age(Sex,Length,Diameter,Height,Whole_weght,shucked_weght,visc_wet,shell_weight):    features = np.array([[Sex,Length,Diameter,Height,Whole_weght,shucked_weght,visc_wet,shell_weight]])    pred = dtr.predict(features).reshape(1,-1)    return pred[0]Sex = 2Length = 8.0Diameter = 4.0Height = 6.0Whole_weght = 10.0shucked_weght = 20.0visc_wet = 20.0shell_weight = 15.0prediciton = prediction_age(Sex,Length,Diameter,Height,Whole_weght,shucked_weght,visc_wet,shell_weight)if prediciton[0] == 0:    print("{} is a Male".format(prediciton))elif prediciton[0] == 1:    print("{} is a Female".format(prediciton))else:    print("{} is a Ifant".format(prediciton))[14.] is a IfantC:UsersNoor SaeedAppDataLocalProgramsPythonPython310libsite-packagessklearnbase.py:450: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names warnings.warn([102]import picklepickle.dump(dtr,open('model.pkl','wb'))p

Flask Code

from flask import Flask,request, render_template

import numpy as npimport pandas as pdimport pickle# load modlemodel = pickle.load(open('model.pkl','rb'))

#create app

app = Flask(__name__)

@app.route('/')def index():    return render_template('index.html')@app.route('/predict',methods=['POST'])def predict():    # sex, length, diameter, height, wholeWeight, Shuckedweight, Visceraweight, Shellweight    sex = int(request.form['sex'])    length = float(request.form['length'])    diameter = float(request.form['diameter'])    height = float(request.form['height'])    wholeWeight = float(request.form['wholeWeight'])    Shuckedweight = float(request.form['Shuckedweight'])    Visceraweight = float(request.form['Visceraweight'])    Shellweight = float(request.form['Shellweight'])    features = np.array([[sex, length, diameter, height, wholeWeight, Shuckedweight, Visceraweight, Shellweight]])    age = model.predict(features).reshape(1,-1)[0]    return render_template('index.html',age = age)# python mainif __name__ == "__main__":    app.run(debug=True)

Front End (HTML Bootstrap)

                                integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T"        crossorigin="anonymous">
    
Abalone Age Prediction Model        
         
                
                    Sex                                            Male                        Female                        Infant                                                    
                    Length                                                    
                    Diameter                                                     
            
                Height                                        
                Whole Weight                                        
                Shucked weight                                            
            
                Viscera weight                                        
                Shell weight                                            Get Abalone Age    
    {% if age %}            
                    
            
Abalone Age: {{age}}            
On the Basis of your input we recommend you the best possible age of this abalone                          {% else %}    
sorry there was an error    {% endif %}