From 78478521e54066b5b3b17b84720272c4bfc263c9 Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:10:35 +0530
Subject: [PATCH 1/7] Update README.md

---
 README.md | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 5dc2cb7..ba67af7 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,9 @@
 # Spam-Detection-using-NLP-ML-models
-This jupyter notebook has various ML classification models to detected a mail as spam or not
+_This jupyter notebook has a ML classification model to detected a mail as spam or not._
+_Our goal is to build a predictive model which will determine whether a text message is spam or ham. For the code, see here._
+
+## Introduction
+
+In recent times, unwanted commercial bulk emails called spam has become a huge problem on the internet. The person sending the spam messages is referred to as the spammer. Such a person gathers email addresses from different websites, chatrooms, and viruses. Spam prevents the user from making full and good use of time, storage capacity and network bandwidth. 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. The menace of spam email is on the increase on yearly basis and is responsible for over 77% of the whole global email traffic. Users who receive spam emails that they did not request find it very irritating. It is also resulted to untold financial loss to many users who have fallen victim of internet scams and other fraudulent practices.
+
+## 1. Inspecting the dataset

From db6e51d33009241d85a67fde1f19c0b3b019cae0 Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:31:32 +0530
Subject: [PATCH 2/7] Update README.md

---
 README.md | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/README.md b/README.md
index ba67af7..fa1a8b2 100644
--- a/README.md
+++ b/README.md
@@ -7,3 +7,21 @@ _Our goal is to build a predictive model which will determine whether a text mes
 In recent times, unwanted commercial bulk emails called spam has become a huge problem on the internet. The person sending the spam messages is referred to as the spammer. Such a person gathers email addresses from different websites, chatrooms, and viruses. Spam prevents the user from making full and good use of time, storage capacity and network bandwidth. 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. The menace of spam email is on the increase on yearly basis and is responsible for over 77% of the whole global email traffic. Users who receive spam emails that they did not request find it very irritating. It is also resulted to untold financial loss to many users who have fallen victim of internet scams and other fraudulent practices.
 
 ## 1. Inspecting the dataset
+
+Taking a data which is categorized in to ham or spam with the text in data.
+ ### a. Importing the required modules/packages
+ ### b. Loading file and looking into the dimensions of data
+ 
+ ## 2. Text preprocessing
+ 
+There are many feature engineering strategies for transforming text data into features. Some involve assigning each unique word-like term to a feature and counting the number of occurrences per training example. However, if we were to perform this strategy right now, we'd end up with an absurd number of features, a result of the myriad possible terms. The classifier would take too long to train and likely overfit. As a result, each NLP problem requires a tailored approach to determine which terms are relevant and meaningful.
+
+## 3. Vectorizing the Text
+
+In the first part of this series, we explored the most basic type of word vectorizer, the Bag of Words Model, which will not work very well for our Spam or Ham classifier due to its simplicity.
+
+Instead, we will use the TF-IDF vectorizer (Term Frequency — Inverse Document Frequency), a similar embedding technique which takes into account the importance of each term to document.
+
+While most vectorizers have their unique advantages, it is not always clear which one to use. In our case, the TF-IDF vectorizer was chosen for its simplicity and efficiency in vectorizing documents such as text messages.
+
+TF-IDF vectorizes documents by calculating a TF-IDF statistic between the document and each term in the vocabulary. The document vector is constructed by using each statistic as an element in the vector.

From be8a5cd30d4cc61e04d4af50701efde464740e22 Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:34:09 +0530
Subject: [PATCH 3/7] Add files via upload

---
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 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 1.png

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From 95f3f45525bf63ac239b786c7e9b200aa31d82f4 Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:35:40 +0530
Subject: [PATCH 4/7] Delete 1.png

---
 1.png | Bin 8011 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 1.png

diff --git a/1.png b/1.png
deleted file mode 100644
index 1d711ed7f8b21f6514e516a0ecea33a922f58815..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 355228b007656d90a638894baa29b7089a018d6b Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:37:02 +0530
Subject: [PATCH 5/7] Uploading files

---
 images/1.png | Bin 0 -> 8011 bytes
 images/2.png | Bin 0 -> 24689 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 images/1.png
 create mode 100644 images/2.png

diff --git a/images/1.png b/images/1.png
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From 794718b5bd9258a93b2a507eb3f3c41795ffc0f8 Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:38:26 +0530
Subject: [PATCH 6/7] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index fa1a8b2..e08213e 100644
--- a/README.md
+++ b/README.md
@@ -25,3 +25,5 @@ Instead, we will use the TF-IDF vectorizer (Term Frequency — Inverse Document
 While most vectorizers have their unique advantages, it is not always clear which one to use. In our case, the TF-IDF vectorizer was chosen for its simplicity and efficiency in vectorizing documents such as text messages.
 
 TF-IDF vectorizes documents by calculating a TF-IDF statistic between the document and each term in the vocabulary. The document vector is constructed by using each statistic as an element in the vector.
+
+![Vectorizing text](https://github.com/Ratna04priya/Spam-Detection-using-NLP-ML-models/blob/master/images/1.png)

From 2b9cb937eb2d2833fc95c78bafa35c22f85bae74 Mon Sep 17 00:00:00 2001
From: Ratna Priya <ratnpriya1998@gmail.com>
Date: Thu, 24 Oct 2019 10:43:54 +0530
Subject: [PATCH 7/7] Update README.md

---
 README.md | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/README.md b/README.md
index e08213e..cd3f1b2 100644
--- a/README.md
+++ b/README.md
@@ -27,3 +27,14 @@ While most vectorizers have their unique advantages, it is not always clear whic
 TF-IDF vectorizes documents by calculating a TF-IDF statistic between the document and each term in the vocabulary. The document vector is constructed by using each statistic as an element in the vector.
 
 ![Vectorizing text](https://github.com/Ratna04priya/Spam-Detection-using-NLP-ML-models/blob/master/images/1.png)
+
+The TF-IDF statistic for term i in document j is calculated as follows:
+
+![Formula](https://github.com/Ratna04priya/Spam-Detection-using-NLP-ML-models/blob/master/images/2.png)
+
+After settling with TF-IDF, we must decide the granularity of our vectorizer.
+We use Counter vectorizer.
+
+## 4. Gradient Boosting
+
+Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.