Spam Detection
Objective: -
Whenever you submit details about your email or contact number on any platform, it has become easy for those platforms to market their products by advertising them by sending emails or by sending messages directly to your contact number. This results in lots of spam alerts and notifications in your inbox. This is where the task of spam detection comes in.
Spam detection means detecting spam messages or emails by understanding text content so that you can only receive notifications about messages or emails that are very important to you. If spam messages are found, they are automatically transferred to a spam folder and you are never notified of such alerts. This helps to improve the user experience, as many spam alerts can bother many users.
The goal of this challenge is to build a machine learning model that classify whether the mail is spam or not. Further accurate classification of mail can help in identifying the spam.
Dataset: -
The dataset is openly available at Kaggle.
Two real-valued features are computed for each cell nucleus:
- Class
- Message
Step 1: Import all the required libraries
- Pandas : In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis and storing in a proper way. In particular, it offers data structures and operations for manipulating numerical tables and time series
- Sklearn : Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.
- Pickle : Python pickle module is used for serializing and de-serializing a Python object structure. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script.
- Seaborn : Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
- Matplotlib : Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
#Loading libraries
import pandas as pd
import seaborn as sns
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.naive_bayes import BernoulliNB
import warnings
warnings.filterwarnings('ignore')Step 2 : Read dataset and basic details of dataset
Goal:- In this step we are going to read the dataset, view the dataset and analysis the basic details like total number of rows and columns, what are the column data types and see to need to create new column or not.
In this stage we are going to read our problem dataset and have a look on it.
#loading the dataset
try:
df = pd.read_csv('C:/My Sample Notebook/Notebook Template/Spam Detection/data/spam.csv',encoding= 'latin-1') #Path for the file
print('Data read done successfully...')
except (FileNotFoundError, IOError):
print("Wrong file or file path")Data read done successfully...# To view the content inside the dataset we can use the head() method that returns a specified number of rows, string from the top.
# The head() method returns the first 5 rows if a number is not specified.
df.head()
Step3: Data Preprocessing
Why need of Data Preprocessing?
Preprocessing data is an important step for data analysis. The following are some benefits of preprocessing data:
- It improves accuracy and reliability. Preprocessing data removes missing or inconsistent data values resulting from human or computer error, which can improve the accuracy and quality of a dataset, making it more reliable.
- It makes data consistent. When collecting data, it’s possible to have data duplicates, and discarding them during preprocessing can ensure the data values for analysis are consistent, which helps produce accurate results.
- It increases the data’s algorithm readability. Preprocessing enhances the data’s quality and makes it easier for machine learning algorithms to read, use, and interpret it.
Why we drop column?
By analysing the first five rows we found that there is a column named [‘Unnamed: 2’],[‘Unnamed: 3’] and [‘Unnamed: 4’], it contains unuseful information which isn’t good for our model, se we gonna drop it using the below method:
df = df.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'], axis =1)Axis are defined for arrays with more than one dimension. A 2-dimensional array has two corresponding axes: the first running vertically downwards across rows (axis 0) and the second running horizontally across columns (axis 1).
After we read the data, we can look at the data using:
# count the total number of rows and columns.
print ('The train data has {0} rows and {1} columns'.format(df.shape[0],df.shape[1]))The train data has 5572 rows and 2 columns
By analysing the problem statement and the dataset, we get to know that the target variable is “CLASS” column.
df['class'].value_counts()ham 4825
spam 747
Name: class, dtype: int64
The df.value_counts() method counts the number of types of values a particular column contains.
df.shape(5572, 2)
The df.shape method shows the shape of the dataset.
df.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5572 entries, 0 to 5571
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 class 5572 non-null object
1 message 5572 non-null object
dtypes: object(2)
memory usage: 87.2+ KB
The df.info() method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.
df.iloc[1]class ham
message Ok lar... Joking wif u oni...
Name: 1, dtype: object
df.iloc[ ] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. The iloc property gets, or sets, the value(s) of the specified indexes.
Data Type Check for every column
Why data type check is required?
Data type check helps us with understanding what type of variables our dataset contains. It helps us with identifying whether to keep that variable or not. If the dataset contains contiguous data, then only float and integer type variables will be beneficial and if we have to classify any value then categorical variables will be beneficial.
objects_cols = ['object']
objects_lst = list(df.select_dtypes(include=objects_cols).columns)print("Total number of categorical columns are ", len(objects_lst))
print("There names are as follows: ", objects_lst)Total number of categorical columns are 2
There names are as follows: ['class', 'message']int64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)print("Total number of numerical columns are ", len(int64_lst))
print("There names are as follows: ", int64_lst)Total number of numerical columns are 0
There names are as follows: []float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)print("Total number of float64 columns are ", len(float64_lst))
print("There name are as follow: ", float64_lst)Total number of float64 columns are 0
There name are as follow: []
Step 2 Insights: -
- We have total 2 features where all are categorical type.
- Drop Unnamed 2, Unnamed 3 and Unnamed 4.
After this step we have to calculate various evaluation parameters which will help us in cleaning and analysing the data more accurately.
Step 3: Descriptive Analysis
Goal/Purpose: Finding the data distribution of the features. Visualization helps to understand data and also to explain the data to another person.
Things we are going to do in this step:
- Mean
- Median
- Mode
- Standard Deviation
- Variance
- Null Values
- NaN Values
- Min value
- Max value
- Count Value
- Quatilers
- Correlation
- Skewness
df.describe()
The df.describe() method returns description of the data in the DataFrame. If the DataFrame contains numerical data, the description contains these information for each column: count — The number of not-empty values. mean — The average (mean) value.
Measure the variability of data of the dataset
Variability describes how far apart data points lie from each other and from the center of a distribution.
Null and Nan values
- Null Values
A null value in a relational database is used when the value in a column is unknown or missing. A null is neither an empty string (for character or datetime data types) nor a zero value (for numeric data types).
df.isnull().sum()class 0
message 0
dtype: int64
As we notice that there are no null values in our dataset.
- Nan Values
NaN, standing for Not a Number, is a member of a numeric data type that can be interpreted as a value that is undefined or unrepresentable, especially in floating-point arithmetic.
df.isna().sum()class 0
message 0
dtype: int64
As we notice that there are no nan values in our dataset.
Another way to remove null and nan values is to use the method “df.dropna(inplace=True)”.
df['class']0 ham
1 ham
2 spam
3 ham
4 ham
...
5567 spam
5568 ham
5569 ham
5570 ham
5571 ham
Name: class, Length: 5572, dtype: object
Step 3 Insights: -
With the statistical analysis we have found that the data have a lot of skewness in them all the columns are positively skewed with mostly zero variance.
Statistical analysis is little difficult to understand at one glance so to make it more understandable we will perform visulatization on the data which will help us to understand the process easily.
Why we are calculating all these metrics?
Mean / Median /Mode/ Variance /Standard Deviation are all very basic but very important concept of statistics used in data science. Almost all the machine learning algorithm uses these concepts in data preprocessing steps. These concepts are part of descriptive statistics where we basically used to describe and understand the data for features in Machine learning
sns.countplot(x = df['class'])<AxesSubplot: xlabel='class', ylabel='count'>
We can observe that most of the messages are not spam(ham).
Step 4: Data Exploration
Goal/Purpose:
Graphs we are going to develop in this step
- Histogram of all columns to check the distrubution of the columns
- Distplot or distribution plot of all columns to check the variation in the data distribution
- Heatmap to calculate correlation within feature variables
- Boxplot to find out outlier in the feature columns
1. Histogram
A histogram is a bar graph-like representation of data that buckets a range of classes into columns along the horizontal x-axis.The vertical y-axis represents the number count or percentage of occurrences in the data for each column
Histogram Insight: -
Histogram helps in identifying the following:
- View the shape of your data set’s distribution to look for outliers or other significant data points.
- Determine whether something significant has boccurred from one time period to another.
Why Histogram?
It is used to illustrate the major features of the distribution of the data in a convenient form. It is also useful when dealing with large data sets (greater than 100 observations). It can help detect any unusual observations (outliers) or any gaps in the data.
From the above graphical representation we can identify that the highest bar represents the outliers which is above the maximum range.
We can also identify that the values are moving on the right side, which determines positive and the centered values determines normal skewness.
All categorical column so can’t plot graph.
2. Distplot
A Distplot or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables. The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it
Distplot Insights: -
Above is the distrution bar graphs to confirm about the statistics of the data about the skewness, the above results are:
- 1 column i.e CLASSS which is our target variable ~ which is also +ve skewed. In that case we’ll need to log transform this variable so that it becomes normally distributed. A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables
Why Distplot?
Skewness is demonstrated on a bell curve when data points are not distributed symmetrically to the left and right sides of the median on a bell curve. If the bell curve is shifted to the left or the right, it is said to be skewed.
We can observe that the bell curve is shifted to left we indicates positive skewness.As all the column are positively skewed we don’t need to do scaling.
Let’s proceed and check the distribution of the target variable.
All categorical column so can’t plot graph.
3. Heatmap
A heatmap (or heat map) is a graphical representation of data where values are depicted by color.Heatmaps make it easy to visualize complex data and understand it at a glance
Correlation — A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, when one variable increases as the other variable increases, or one variable decreases while the other decreases.
Correlation can have a value:
- 1 is a perfect positive correlation
- 0 is no correlation (the values don’t seem linked at all)
- -1 is a perfect negative correlation
Heatmap insights: -
As we know, it is recommended to avoid correlated features in your dataset. Indeed, a group of highly correlated features will not bring additional information (or just very few), but will increase the complexity of the algorithm, hence increasing the risk of errors.
Why Heatmap?
Heatmaps are used to show relationships between two variables, one plotted on each axis. By observing how cell colors change across each axis, you can observe if there are any patterns in value for one or both variables.
All categorical column so can’t plot graph.
4. Boxplot
A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile [Q1], median, third quartile [Q3] and “maximum”).
Basically, to find the outlier in a dataset/column.
The dark points are known as Outliers. Outliers are those data points that are significantly different from the rest of the dataset. They are often abnormal observations that skew the data distribution, and arise due to inconsistent data entry, or erroneous observations.
Boxplot Insights: -
- Sometimes outliers may be an error in the data and should be removed. In this case these points are correct readings yet they are different from the other points that they appear to be incorrect.
- The best way to decide wether to remove them or not is to train models with and without these data points and compare their validation accuracy.
- So we will keep it unchanged as it won’t affect our model.
Here, we can see that most of the variables possess outlier values. It would take us days if we start treating these outlier values one by one. Hence, for now we’ll leave them as is and let our algorithm deal with them. As we know, tree-based algorithms are usually robust to outliers.
Why Boxplot?
Box plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. They are built to provide high-level information at a glance, offering general information about a group of data’s symmetry, skew, variance, and outliers.
In the next step we will divide our cleaned data into training data and testing data.
As the problem is based on Natural Language Processing so data visualization isn’t needed because majority columns are categorical.
We only need the content and class column from the dataset for the rest of the task. So let’s select both the columns and move further:
Step 2: Data Preparation
Goal:-
Tasks we are going to in this step:
- Now we will spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
- Split dataset into train and test dataset.
- Scaling on train dataset.
1. Now we spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
# Separate target and feature column in X and y variable
# X will be the features
X = np.array(df["message"])
#y will be the target variable
y = np.array(df["class"])Xarray(['Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',
'Ok lar... Joking wif u oni...',
"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's",
..., 'Pity, * was in mood for that. So...any other suggestions?',
"The guy did some bitching but I acted like i'd be interested in buying something else next week and he gave it to us for free",
'Rofl. Its true to its name'], dtype=object)yarray(['ham', 'ham', 'spam', ..., 'ham', 'ham', 'ham'], dtype=object)# Check the shape of X and y variable
X.shape, y.shape((5572,), (5572,))
Machines cannot understand characters and words. So when dealing with text data we need to represent it in numbers to be understood by the machine. Countvectorizer is a method to convert text to numerical data. To show you how it works let’s take an example:
text = [‘Hello my name is james, this is my python notebook’]
The text is transformed to a sparse matrix as shown below.
- Hello is james my name notebook python this
- 1 2 1 2 1 1 1 1
from sklearn.feature_extraction.text import CountVectorizer
count_vec = CountVectorizer()
X = count_vec.fit_transform(X)With CountVectorizer we are converting raw text to a numerical vector representation of words and n-grams. This makes it easy to directly use this representation as features (signals) in Machine Learning tasks such as for text classification and clustering.
2. Spliting the dataset in training and testing data.
Here we are spliting our dataset into 80/20 percentage where 80% dataset goes into the training part and 20% goes into testing part.
# Split the X and y into X_train, X_test, y_train, y_test variables with 80-20% split.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Check shape of the splitted variables
X_train.shape, X_test.shape, y_train.shape, y_test.shape((4457, 8672), (1115, 8672), (4457,), (1115,))
Insights: -
Train test split technique is used to estimate the performance of machine learning algorithms which are used to make predictions on data not used to train the model.It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small dataset and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced.
In the next step we will train our model on the basis of our training and testing data.
Step 3: Model Training
Goal:
In this step we are going to train our dataset on different classification algorithms. As we know that our target variable is in discrete format so we have to apply classification algorithm. Target variable is a category like filtering.In our dataset we have the outcome variable or Dependent variable i.e Y having only two set of values, either 1 (Spam) or 0(Not Spam). So we will use Classification algorithm**
Algorithms we are going to use in this step
- Bernoulli Naive Bayes
- Linear SVC
- Random Forest Classification
K-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset. There are commonly used variations on cross-validation, such as stratified and repeated, that are available in scikit-learn
# Define kfold with 10 split
cv1 = KFold(n_splits=10, shuffle=True, random_state=42)The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).
1. Bernoulli Naive Bayes
Naive Bayes is a supervised machine learning algorithm to predict the probability of different classes based on numerous attributes. It indicates the likelihood of occurrence of an event. Naive Bayes is also known as conditional probability.
Bernoulli Naive Bayes is a part of the Naive Bayes family. It is based on the Bernoulli Distribution and accepts only binary values, i.e., 0 or 1. If the features of the dataset are binary, then we can assume that Bernoulli Naive Bayes is the algorithm to be used.
Train set cross-validation
#Using Bernoulli Naive Bayes algorithm to the Training Set
from sklearn.naive_bayes import BernoulliNB
model = BernoulliNB()
model.fit(X_train, y_train)BernoulliNB()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
BernoulliNB
BernoulliNB()#Accuracy check of trainig data
#Get R2 score
model.score(X_train, y_train)0.9876598608929773#Accuracy of test data
model.score(X_test, y_test)0.97847533632287
Prediction
Now we will perform prediction on the dataset using Logistic Regression.
# Predict the values on X_test_scaled dataset
y_predicted = model.predict(X_test)Various parameters are calculated for analysing the predictions.
- Confusion Matrix 2)Classification Report 3)Accuracy Score 4)Precision Score 5)Recall Score 6)F1 Score
Confusion Matrix
A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes. It plots a table of all the predicted and actual values of a classifier.
This diagram helps in understanding the concept of confusion matrix.
# Constructing the confusion matrix.
from sklearn.metrics import confusion_matrix#confusion matrix btw y_test and y_predicted
cm = confusion_matrix(y_test,y_predicted)#We are creating Confusion Matrix on heatmap to have better understanding
# sns.heatmap(cm,cmap = 'Red') ~ to check for available colors
sns.set(rc = {'figure.figsize':(5,5)})
sns.heatmap(cm,cmap = 'icefire_r', annot = True, cbar=False, linecolor='Black', linewidth = 2)
plt.title("Confusion matrix")
plt.xlabel('Predicted CLass')
plt.ylabel('True Class')Text(29.75, 0.5, 'True Class')
sns.heatmap(cm/np.sum(cm), annot=True,
fmt='.2%', cmap='Blues', cbar = False)<AxesSubplot: >
Evaluating all kinds of evaluating parameters.
Classification Report :
A classification report is a performance evaluation metric in machine learning. It is used to show the precision, recall, F1 Score, and support of your trained classification model.
F1_score :
The F1 score is a machine learning metric that can be used in classification models.
Precision_score :
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0.
Recall_score :
Recall score is used to measure the model performance in terms of measuring the count of true positives in a correct manner out of all the actual positive values. Precision-Recall score is a useful measure of success of prediction when the classes are very imbalanced.
# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score, accuracy_score, precision_score,recall_score
from sklearn.metrics import confusion_matrix
print("The model used is Bernoulli Naive Bayes")
l_acc = accuracy_score(y_test, y_predicted)
print("\nThe accuracy is: {}".format(l_acc))
prec = precision_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The precision is: {}".format(prec))
rec = recall_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The recall is: {}".format(rec))
f1 = f1_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The F1-Score is: {}".format(f1))
c1 = classification_report(y_test, y_predicted)
print("Classification Report is:")
print()
print(c1)The model used is Bernoulli Naive Bayes
The accuracy is: 0.97847533632287
The precision is: 0.9921875
The recall is: 0.8466666666666667
The F1-Score is: 0.9136690647482014
Classification Report is:
precision recall f1-score support
ham 0.98 1.00 0.99 965
spam 0.99 0.85 0.91 150
accuracy 0.98 1115
macro avg 0.98 0.92 0.95 1115
weighted avg 0.98 0.98 0.98 1115
2. Linear SVC
The objective of a Linear SVC (Support Vector Classifier) is to fit to the data you provide, returning a “best fit” hyperplane that divides, or categorizes, your data. From there, after getting the hyperplane, you can then feed some features to your classifier to see what the “predicted” class is. This makes this specific algorithm rather suitable for our uses, though you can use this for many situations.
#Using KNeighborsClassifier Method of neighbors class to use Nearest Neighbor algorithm
from sklearn.svm import LinearSVC
classifier = LinearSVC()
classifier.fit(X_train, y_train)LinearSVC()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearSVC
LinearSVC()#Accuracy check of trainig data
#Get R2 score
classifier.score(X_train, y_train)1.0#Accuracy of test data
classifier.score(X_test, y_test)0.9820627802690582
Prediction
# Predict the values on X_test_scaled dataset
y_predicted = classifier.predict(X_test)# Constructing the confusion matrix.
from sklearn.metrics import confusion_matrix#Confusion matrix btw y_test and y_predicted
cm = confusion_matrix(y_test,y_predicted)#We are drawing cm on heatmap to have better understanding
# sns.heatmap(cm,cmap = 'Red') ~ to check for available colors
sns.heatmap(cm,cmap = 'icefire_r', annot = True, fmt= 'd', cbar=False, linecolor='Black', linewidth = 2)
plt.title("Confusion matrix")
plt.xlabel('Predicted CLass')
plt.ylabel('True Class')Text(29.75, 0.5, 'True Class')
sns.heatmap(cm/np.sum(cm), annot=True,
fmt='.2%', cmap='Blues', cbar = False)<AxesSubplot: >
Evaluating all kinds of evaluating parameters.
# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score, accuracy_score, precision_score,recall_score
from sklearn.metrics import confusion_matrix
print("The model used is LinearSVC")
k_acc = accuracy_score(y_test, y_predicted)
print("\nThe accuracy is: {}".format(k_acc))
prec = precision_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The precision is: {}".format(prec))
rec = recall_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The recall is: {}".format(rec))
f1 = f1_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The F1-Score is: {}".format(f1))
c1 = classification_report(y_test, y_predicted)
print("Classification Report is:")
print()
print(c1)The model used is LinearSVC
The accuracy is: 0.9820627802690582
The precision is: 0.9850746268656716
The recall is: 0.88
The F1-Score is: 0.9295774647887323
Classification Report is:
precision recall f1-score support
ham 0.98 1.00 0.99 965
spam 0.99 0.88 0.93 150
accuracy 0.98 1115
macro avg 0.98 0.94 0.96 1115
weighted avg 0.98 0.98 0.98 1115
3. Random Forest Classifier
Random Forest is a powerful and versatile supervised machine learning algorithm that grows and combines multiple decision trees to create a “forest.” It can be used for both classification and regression problems in R and Python.
Random Forest and Decision Tree Algorithm are considered best for the data that has outliers.
#Using RandomForestClassifier method of ensemble class to use Random Forest Classification algorithm
from sklearn.ensemble import RandomForestClassifier
#clas = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
clas = RandomForestClassifier()
clas.fit(X_train, y_train)RandomForestClassifier()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier
RandomForestClassifier()#Accuracy check of trainig data
#Get R2 score
clas.score(X_train, y_train)1.0#Accuracy of test data
clas.score(X_test, y_test)0.9748878923766816
Prediction
# predict the values on X_test_scaled dataset
y_predicted = clas.predict(X_test)# Constructing the confusion matrix.
from sklearn.metrics import confusion_matrix#confusion matrix btw y_test and y_predicted
cm = confusion_matrix(y_test,y_predicted)#We are drawing cm on heatmap to have better understanding
# sns.heatmap(cm,cmap = 'Red') ~ to check for available colors
sns.heatmap(cm,cmap = 'icefire_r', annot = True, fmt= 'd', cbar=False, linecolor='Black', linewidth = 2)
plt.title("Confusion matrix")
plt.xlabel('Predicted CLass')
plt.ylabel('True Class')Text(29.75, 0.5, 'True Class')
sns.heatmap(cm/np.sum(cm), annot=True,
fmt='.2%', cmap='Blues', cbar = False)<AxesSubplot: >
Evaluating all kinds of evaluating parameters.
# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score, accuracy_score, precision_score,recall_score
from sklearn.metrics import confusion_matrix
print("The model used is Random Forest Classifier")
r_acc = accuracy_score(y_test, y_predicted)
print("\nThe accuracy is {}".format(r_acc))
prec = precision_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The precision is {}".format(prec))
rec = recall_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The recall is {}".format(rec))
f1 = f1_score(y_test, y_predicted,average="binary", pos_label="spam")
print("The F1-Score is {}".format(f1))
c1 = classification_report(y_test, y_predicted)
print("Classification Report is:")
print()
print(c1)The model used is Random Forest Classifier
The accuracy is 0.9748878923766816
The precision is 1.0
The recall is 0.8133333333333334
The F1-Score is 0.8970588235294117
Classification Report is:
precision recall f1-score support
ham 0.97 1.00 0.99 965
spam 1.00 0.81 0.90 150
accuracy 0.97 1115
macro avg 0.99 0.91 0.94 1115
weighted avg 0.98 0.97 0.97 1115
Insight: -
cal_metric=pd.DataFrame([l_acc,k_acc,r_acc],columns=["Score in percentage"])
cal_metric.index=['Bernoulli Naive Bayes',
'LinearSVC',
'Random Forest']
cal_metric
- As you can see with our LinearSVC Model(98.20%) we are getting a better result even for the recall (0.98 or 98%) which is the most tricky part.
- So we gonna save our model with LinearSVC Model
Step 4: Save Model
Goal:- In this step we are going to save our model in pickel format file.
import pickle
pickle.dump(model , open('Spam_Detection_BernoulliNB.pkl', 'wb'))
pickle.dump(classifier , open('Spam_Detection_LinearSVC.pkl', 'wb'))
pickle.dump(clas , open('Spam_Detection_Logistic.pkl', 'wb'))import pickle
def model_prediction(features):
features = count_vec.transform([features]).toarray()
pickled_model = pickle.load(open('C:/My Sample Notebook/Notebook Template/Spam Detection/model/Spam_Detection_BernoulliNB.pkl', 'rb'))
Message = str(list(pickled_model.predict(features)))
return str(f'The Message is {Message}')
We can test our model by giving our own parameters or features to predict.
message = "Free entry in 2 a wkly comp to win FA Cup"model_prediction(message)"The Message is ['spam']"
Conclusion
After observing the problem statement we have build an efficient model to overcome it. The above model helps in classifying the whether the message is Spam or not. The accuracy for the prediction is 98.20% and it signifies the accurate classification of the message.
Checkout whole project code here (github repo).
🚀 Unlock Your Dream Job with HiDevs Community!
🔍 Seeking the perfect job? HiDevs Community is your gateway to career success in the tech industry. Explore free expert courses, job-seeking support, and career transformation tips.
💼 We offer an upskill program in Gen AI, Data Science, Machine Learning, and assist startups in adopting Gen AI at minimal development costs.
🆓 Best of all, everything we offer is completely free! We are dedicated to helping society.
Book free of cost 1:1 mentorship on any topic of your choice — topmate
✨ We dedicate over 30 minutes to each applicant’s resume, LinkedIn profile, mock interview, and upskill program. If you’d like our guidance, check out our services here
💡 Join us now, and turbocharge your career!
Deepak Chawla LinkedIn
Vijendra Singh LinkedIn
Yajendra Prajapati LinkedIn
YouTube Channel
Instagram Page
HiDevs LinkedIn
Project Youtube Link
