- Business Understanding
- Data Understanding
- Data Analysis and Visualization
- Data Preparation and Preprocessing
- Modeling
- Evaluation
The primary objective of this project is to classify tweets into predefined categories. This involves understanding the nuances and context of each tweet to accurately assign it to the correct category.
In this phase, we examine the dataset provided for the project. This includes:
- Checking for missing values.
- Understanding the structure of the dataset.
- Exploring some basic statistics to get an overview of the data.
To gain insights into the data, we visualize and analyze it. This helps in understanding patterns and distributions, which can guide the preprocessing and modeling steps.
Text data needs to be preprocessed to be useful for modeling. The steps involved are:
- Preprocess the text data: Clean the text data to remove any noise.
- Tokenize the text: Split the text into individual words or tokens.
- Remove stopwords: Remove common words that do not contribute to the meaning of the text.
- Feature extraction: Use techniques like CountVectorizer and TF-IDF to convert text into numerical features.
- Split the dataset: Divide the dataset into training and testing sets for model validation.
Select and train suitable classification models using the training data. The models considered in this project are:
- Logistic Regression
- Naive Bayes
- Deep Learning models
Evaluate the trained models using appropriate evaluation metrics such as:
- Accuracy
- Precision
- Recall
- F1-score
Compare the performance of different models and pipeline choices to select the best model for classifying tweets.
This README provides an overview of the steps and objectives involved in the Tweet Classification Project. Each phase is crucial for ensuring the accuracy and reliability of the final classification models.