Data Science Projects

A curated collection of beginner-friendly data science projects with real datasets, clear explanations, and working code. Learn by building.

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Table of Contents

• Why This Repo?
• Learning Path
• Getting Started
• License

Why This Repo?

You will struggle:

• Data Cleaning and Preprocessing -- preparing real-world messy data
• Exploratory Data Analysis -- visualizations and statistical insights
• Machine Learning -- classification, regression, and anomaly detection
• Deep Learning -- CNNs, transfer learning, and NLP models

Learning Path

Start from the top and work your way down. Projects are ordered by difficulty within each level.

Level 1 -- Fundamentals

Get comfortable with pandas, sklearn, and basic ML workflows.

#	Project	What You'll Learn	Category
1	Titanic Survival Prediction	EDA, data cleaning, feature engineering, 7 classifiers, GridSearchCV	Classification
3	Customer Churn	Logistic regression from scratch, prediction on new data	Classification
4	Heart Failure Prediction	Feature analysis, multiple classifiers, model evaluation	Classification
5	Rental Prices of AirBnb	Linear regression, outlier analysis, label encoding	Regression

Level 2 -- Text and NLP

Learn to work with text data, preprocessing pipelines, and NLP techniques.

#	Project	What You'll Learn	Category
6	Message Spam Filtering	TF-IDF, text preprocessing, SVM classification	NLP
7	Cyber-Bullying Prediction	NLP pipeline, GridSearchCV, model comparison	NLP
8	Sentiment Analysis	Logistic regression from scratch, Twitter data, NLTK	NLP
9	AirBnb Reviews Sentimental Analysis	Full NLP pipeline: preprocessing, ML, deep learning, LLMs	NLP

Level 3 -- Computer Vision and Deep Learning

Work with images, neural networks, and pre-trained models.

#	Project	What You'll Learn	Category
10	Gender Classification	EfficientNetV2, transfer learning, Keras	Classification
11	Face Detection	Haar cascades, MTCNN, OpenCV	Computer Vision
12	Face Recognition	LBPH algorithm, real-time webcam recognition	Computer Vision
14	Alzheimer Detection	Clinical data analysis, Random Forest on medical data	Computer Vision

Level 4 -- Advanced Topics

Tackle more complex real-world problems.

#	Project	What You'll Learn	Category
15	Network Intrusion Detection System	Ensemble methods, XGBoost, KDD Cup dataset	Anomaly Detection
16	Object Detection	YOLOv8, Faster R-CNN, RetinaNet, Detectron2	Computer Vision
17	Pose Estimation	YOLOv8, MediaPipe, activity classification	Computer Vision
18	Robotics and Computer Integrated Manufacturing	MobileNetV2, transfer learning, industrial imaging	Robotics

Getting Started

Prerequisites

• Python 3.9+
• Jupyter Notebook or JupyterLab

Core Libraries

pip install pandas numpy matplotlib seaborn scikit-learn jupyter

Additional Libraries (by project type)

Project Type	Install
Deep Learning	pip install tensorflow keras
Computer Vision	pip install opencv-python
NLP	pip install nltk
Object Detection	pip install ultralytics

Each project has its own requirements.txt for exact dependencies:

cd "Project Folder Name"
pip install -r requirements.txt
jupyter notebook

Quick Start

git clone https://github.com/Behniash/Beginner-Data-Science-Projects.git
cd Beginner-Data-Science-Projects

# Pick a project and run it
cd "Iris Flower Classification"
pip install -r requirements.txt
jupyter notebook

License

This project is licensed under the MIT License -- use it freely for learning, teaching, or building.

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