A collection of end-to-end Data Science projects completed during DevTown training — covering classification, sentiment analysis, and real-world datasets.
Dataset: Invistico Airlines (Invisitico_Airline.csv)
Objective: Predict whether a customer is satisfied or dissatisfied with airline service based on various features.
Key Steps:
- Exploratory Data Analysis (EDA) on passenger demographics & flight data
- Data cleaning and preprocessing (handling nulls, encoding)
- Built and evaluated classification models
- Feature importance analysis
Tech Stack: Python Pandas Scikit-learn Matplotlib Seaborn
Objective: Classify movie reviews as positive or negative using Natural Language Processing.
Key Steps:
- Text preprocessing (tokenization, stopword removal)
- Feature extraction using Bag of Words / TF-IDF
- Naive Bayes classifier training and evaluation
- Accuracy and confusion matrix analysis
Tech Stack: Python NLTK Scikit-learn Pandas
# Clone the repo
git clone https://github.com/Sreeharipavithran/DevTown-Data_Science_Training-Project.git
# Install dependencies
pip install -r requirements.txt
# Open notebooks
jupyter notebook├── Invistico-Airline Customer Satisfaction Classification ML Model Using RandomForest.ipynb
├── Invisitico_Airline.csv
├── Movie Review Sentimental Analysis - Naive Bayes.ipynb
└── README.md
