ClearFeelings 😊✈️

ClearFeelings is a sentiment analysis project that utilizes logistic regression to analyze sentiments in the Twitter Airline Sentiment dataset. The project aims to provide insights into the sentiments expressed towards various airlines on Twitter.

#Technologies Used 🛠️

1. Python 🐍:
   Python is a high-level, versatile programming language commonly used in data analysis, machine learning, and web development. It's chosen for its ease of use and extensive libraries for various tasks.
   
2. Logistic Regression 📈:
   Logistic regression is a statistical method used for modeling the probability of binary outcomes. In sentiment analysis, it's often used to classify text into positive or negative sentiments based on input features.
   
3. LIME 🧠:
   LIME, short for Local Interpretable Model-agnostic Explanations, is a technique used to explain the predictions of machine learning models. It provides local interpretations for individual predictions, helping to understand why a model made a particular decision.
   
4. Jupyter Notebook 📓:
   Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It's widely used in data analysis and machine learning workflows due to its interactive nature.
   
5. Matplotlib 📊:
   Matplotlib is a plotting library for Python that provides a MATLAB-like interface for creating a variety of plots and charts. It's used for visualizing data and model outputs in ClearFeelings.
   
6. Seaborn 🌊:
   Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn is used alongside Matplotlib to enhance the visualizations in ClearFeelings.
   

Project Overview 🚀

ClearFeelings employs logistic regression to classify sentiments expressed in tweets regarding airline experiences. The sentiment analysis model is trained on the Twitter Airline Sentiment dataset, allowing users to understand the overall sentiment trends towards different airlines.

Features 🌟

Utilizes logistic regression for sentiment analysis.
Provides interpretability using LIME to explain model predictions.
Visualizes findings using Seaborn and Matplotlib for clear insights.

How to Use 📝

1. Clone the Repository
2. Install Dependencies
3. Run the Jupyter Notebook
4. Follow the Notebook Instructions:
5. Load the Twitter Airline Sentiment dataset. Preprocess the data. Train the logistic regression model. Visualize the findings using Seaborn and Matplotlib. Explain model predictions using LIME.

Output 📊

Feel free to reach out with any questions or suggestions! 📧