Objective:
Build a system to classify tweets into Positive, Negative, or Neutral sentiments using both a traditional ML approach and a transformer-based deep learning model.
? Tech Stack
-Language: Python
-Data Handling & Processing: Pandas, NumPy
-ML & DL Libraries: Scikit-learn, TensorFlow, Transformers (BERT)
-Algorithms & Techniques: TF-IDF, Logistic Regression, BERT Fine-tuning
-Data Visualization: Matplotlib, Seaborn , WordCloud
⚙️ Process Overview:
1️⃣ Data Cleaning & Preprocessing — remove noise, normalize text
2️⃣ EDA — sentiment distribution, word clouds
3️⃣ Model 1: TF-IDF + Logistic Regression (baseline)
4️⃣ Model 2: BERT fine-tuning for sequence classification
5️⃣ Evaluation: Accuracy, classification reports, confusion matrix
6️⃣ Model Saving — packaged both models for future deployment
