https://github.com/hirudikaanupama/email-spam-detection-logistic-regression

This model can predict whether an email is spam or not. The logistic regression machine learning algorithm is used to train this model.
https://github.com/hirudikaanupama/email-spam-detection-logistic-regression

accuracy-score classification classification-report confusionmatrix data-visualization logistic-regression machine-learning roc-curve

Last synced: 25 days ago
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This model can predict whether an email is spam or not. The logistic regression machine learning algorithm is used to train this model.

• Host: GitHub
• URL: https://github.com/hirudikaanupama/email-spam-detection-logistic-regression
• Owner: HirudikaAnupama
• Created: 2024-07-26T21:08:52.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-08-11T18:21:18.000Z (about 1 year ago)
• Last Synced: 2025-06-22T07:44:01.852Z (4 months ago)
• Topics: accuracy-score, classification, classification-report, confusionmatrix, data-visualization, logistic-regression, machine-learning, roc-curve
• Language: Jupyter Notebook
• Homepage:
• Size: 2.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
Email Spam Detection Using Logistic Regression

Most of the logistic regression theory is covered in this project.

Using these theories, We can identify whether emails are spam or not




## Introduction

- When we train the machine learning model we need to follow several steps.

- While training this linear regression machine learning algorithm, we need to follow some steps to make the model accurate and fast. Amoung them are things like,


  ##### _Data Collecting_

  ##### _Data Preprocessing_ 

  ##### _Data Analysis_

  ##### _Split the Data into training and testing_

  ##### _Evaluate the Model_

  ##### _Check model performance_

  ##### _Fine-tune the Model_

  

- A better understanding of these can be obtained from the following introduction and relative code sections related to the introduction can be obtained by observing the code.




### Data Collecting

- We must collect the data we need according to our needs.

- Depending on the target variable (dependent variable/ our predictor variable/ y) we need to collect other data (characteristics/ independent variables/ Features).

### Data Preprocessing 

- After collecting the data we need to clean it,


  ##### _find missing data and fill them_

  ##### _Drop duplicate data_

  ##### _Turn categorical data into numerical or Boolean_

  ##### _Rename columns for easily understand_

  ##### _Separate target value and features_

  

- We can use Encoding method or dummy method for convert categorical data into numerical or boolean.

### Data Analysis

- We can analyze the relationships between the target and the features using plots, graphs, etc.

- We can identify the relationship through the following sample examples.