https://github.com/aaaastark/textclassification-nlp-project

Dataset preparation, Feature Engineering, Model Building, Summary Statistics, Data Exploration by Visualizations, and Save File CSV
https://github.com/aaaastark/textclassification-nlp-project

matplotlib nltk numpy pandas python seaborn sklearn textblob wordcloud

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Dataset preparation, Feature Engineering, Model Building, Summary Statistics, Data Exploration by Visualizations, and Save File CSV

• Host: GitHub
• URL: https://github.com/aaaastark/textclassification-nlp-project
• Owner: aaaastark
• Created: 2022-04-27T11:47:07.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-04-27T11:54:00.000Z (almost 3 years ago)
• Last Synced: 2024-11-15T06:36:23.140Z (3 months ago)
• Topics: matplotlib, nltk, numpy, pandas, python, seaborn, sklearn, textblob, wordcloud
• Language: Jupyter Notebook
• Homepage:
• Size: 1.92 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# TextClassification-NLP-Project

Dataset preparation, Feature Engineering, Model Building, Summary Statistics, Data Exploration by Visualizations, and Save File CSV.

## NOTE: Open the GPU (Google Colab)

# 1. Dataset preparation

*   Data Set Load (Movies - TV)

*   Train, Test and Encoder (Train and Validation)

# 2. Feature Engineering

2.1 Count Vectors as features

2.2 TF-IDF Vectors as features

*   Word level

*   N-Gram level

*   Character level

2.3 Text / NLP based features

# 3. Model Building

3.1   Naive Bayes Classifier (Accuray, Confusion Matrix, Precision, Recall Score, F1 Score)

3.2   Linear Classifier (Accuray, Confusion Matrix, Precision, Recall Score, F1 Score)

3.3   Support Vector Machine (Accuray, Confusion Matrix, Precision, Recall Score, F1 Score)

# 4. Summary Statistics

# 5. Data Exploration by Visualizations

5.1 Word Density

5.2 Rating Text

5.3 Create Word Clouds

*   5.3.1 Word Cloud for Rating (Label) Score is 5, 4, 3, 2, 1

# 6. Save File CSV