Email-Sms-Spam-or-Ham-Classifier

[ Step - I ] . Importing the required lib's and data cleaning

1. Import the data & lib's.

2. Print the head.

3. Shape & description of data.

4. Data information.

5. Renaming the columns.

6. Apply the label encoder Spam -> 1 and Ham ->0

7. Check for the null values, duplicate values , remove the duplicate values.

[ Step - II ] . Exploratory Data Analysis

1. Value counts for the target variable , plot the pie chart.

2. Import the nltk pkg.

3. Count the number of sentences.

4. Descibe our new features.

5. Plot the histogram.

6. Computing the correlations.

[ Step - III ] . Text Preprocessing

1. Text preprocessing.

2. Apply on our dataset.

3. Install the word cloud.

4. Make the word cloud for spam msg's.

5. Make word cloud for ham msg's.

6. Top 30 word's in spam msg's.

7. Top 30 word's in ham msg's.

[ Step - IV ] . Model Building

1. Count vectorizer.

2. Train , Test, Split.

3. Import 3 models

4. Test for Multinomial NB

5. Test for Binomial NB

[Step - V] Model deployment

1. Import pickle