[Feature]: Enhance and merge email spam detection notebook with EDA and NLP improvements #1455

Detection Models/Email Spam Detection/README.md

@@ -1,35 +1,60 @@
-
 # Email Spam Detection
 
-This project aims to detect unwanted emails (spam) from a user's inbox. Email spam detection involves using various techniques to analyze the content, metadata, and patterns of emails to distinguish between spam and legitimate messages.
+This project aims to detect unwanted emails (spam) from a user's inbox. It uses a combination of **Natural Language Processing (NLP)** techniques and machine learning algorithms to analyze the content, metadata, and patterns of emails, distinguishing between spam and legitimate messages.
+
 ## Goal
 
-The goal of email spam detection is to identify and filter out unsolicited emails (spam) from a user's inbox, ensuring that only legitimate and important emails are delivered. This helps in reducing clutter, protecting users from potential phishing attacks, malware, and other malicious activities commonly associated with spam emails.
+The goal is to identify and filter out unsolicited emails (spam) from a user's inbox, ensuring only legitimate and important emails are delivered. This reduces clutter and protects users from phishing attacks, malware, and other malicious activities.
+
 ## Methodology
 
-Utilizing a combination of EDA techniques and machine learning algorithms, we have meticulously analyzed data to discern patterns and correlations associated with email . Key steps include data cleaning, feature engineering, and insightful visualization to extract meaningful insights.
-## Data Preprocessing 
+Using a mix of **Exploratory Data Analysis (EDA)** and **NLP**, the data is analyzed to identify patterns and correlations. Key steps include:
+- Data cleaning
+- Text preprocessing through NLP
+- Feature engineering and insightful visualizations
+
+## Data Preprocessing
 
-Data preprocessing steps include:
+Steps involved:
 1. Stop words removal
-2. Lemmatization/Stemming
-3. Vectorization using TF-IDF
+2. Lemmatization/Stemming (NLP)
+3. Vectorization using **TF-IDF** (NLP)
+4. Tokenization for breaking down email content
+
+## NLP Techniques Used
+
+- **Tokenization**: Breaking down email texts into words
+- **Stemming/Lemmatization**: Reducing words to their base form
+- **TF-IDF**: Transforming text into numerical values based on importance
+- **Bag of Words**: Converting text into features for model building
+
 ## Models Utilized
 
-1. Logistic Regression
-2. Random Forest Regressor
-3. Multinomial Naive Bayes
+1. **Logistic Regression**
+2. **Random Forest Regressor**
+3. **Multinomial Naive Bayes**
+
 ## Libraries Used
 
-1. numpy: For efficient numerical operations
-2. pandas: For data manipulation and analysis
-3. seaborn: For visually appealing statistical graphics
-4. matplotlib: For comprehensive data visualization
-5. Sklearn: For implementing machine learning algorithms
+1. **numpy**: Numerical operations
+2. **pandas**: Data manipulation and analysis
+3. **nltk**: NLP toolkit for text processing
+4. **seaborn**: Statistical visualizations
+5. **matplotlib**: Data visualization
+6. **sklearn**: Machine learning algorithms
+
 ## Results
-1. Logistic Regression : 94%
-2. Random Forest Regressor : 89%
-3. Multinomial Naive Bayes : 96%
+
+1. **Logistic Regression**: 98% accuracy
+2. **Multinomial Naive Bayes**: 97% accuracy
+
+## Enhanced Features
+
+Additional NLP-based features include:
+- Word frequency analysis using **nltk** and **collections.Counter**
+- Character, word, and sentence count analysis
+- Heatmap and distribution plots for word frequency and relationships
 
 ## Conclusion
-Through rigorous analysis and experimentation, it has been determined that Multinomial Naive Bayes model exhibit the highest predictive accuracy for Email Spam detection. 
+
+Through rigorous analysis and the use of advanced **NLP** techniques, the **Multinomial Naive Bayes** model yielded the highest predictive accuracy for email spam detection.