In recent times, unwanted commercial bulk emails called spam has become a huge problem on the internet. The huge volume of spam mails flowing through the computer networks have destructive effects on the memory space of email servers, communication bandwidth, CPU power and user time. To overcome this problem we have made a Spam-Ham classification model using Naive Bayes Classifier.
You can find the dataset used for the project here.
We Executed these 7 Steps, one after the other, to implement our spam classifier:
Step 1: Load the data
Step 2: Data Cleaning
Step 3: Exploratory Data Analysis (EDA)
Step 4: Data Visualization
Step 5: Splitting into train and test set
Step 6: Model Fitting
Step 7: Predictions and Evaluations
This project was built by
🧑💻 Anurag 🧑💻 Samarth 🧑💻 Shashwat 🧑💻 Vikanksh
- R Programming Language
- Text Mining
- Data Visualization
- Natural Language Processing
- Machine learning Algorithm
To learn more about the technolgies used, you can visit
Spam Filtering , Naive Bayes Classifier
Accuracy: 99.78%
Specificity: 99.47%