🧠 Data Science with Python

Welcome to Data Science with Python — a curated portfolio of practical data science and machine learning projects developed by Tega Jarikre.
Each project demonstrates real-world applications of Python in solving problems across insurance, logistics, content moderation, and agricultural analytics.

This repository represents a continuous learning journey in data-driven problem-solving, from raw data wrangling to model deployment.

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🚀 Project Objectives

• Build and evaluate machine learning models across diverse domains
• Apply data preprocessing, feature engineering, and model optimization techniques
• Experiment with classification, regression, and clustering algorithms
• Explore bias detection, economic efficiency, and yield analysis using real datasets
• Strengthen portfolio readiness for data science career roles

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🧩 Repository Structure

Data-Science-with-Python/ │ ├── insurance_premium_prediction/ # Regression models for premium forecasting ├── fake_content_detection/ # NLP + metadata-based bias and fake news detection ├── delivery_delay_classification/ # Predicting early, on-time, or late deliveries ├── earthquake damage classification/ # Predicting low, medium, or high grade earthquake damage ├── china real estate demand prediction/ # Regression models for real estate demand forecasting ├── borehole functionality classification/ # Predicting function, non-functional, or functional needs repair boreholes ├── notebooks/ # Shared EDA, feature engineering, and model experiments ├── scripts/ # Reusable Python utilities ├── data/ # Sample datasets (clean or synthetic) ├── results/ # Visualizations and performance reports ├── requirements.txt # Dependencies for reproducibility └── README.md # You’re here!

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🧰 Tech Stack

Category	Tools & Libraries
Core Language	Python 3.10+
Data Manipulation	Pandas, NumPy
Visualization	Matplotlib, Seaborn, Plotly
Modeling & ML	Scikit-learn, XGBoost, Random Forest, Logistic Regression
NLP & Text Analytics	NLTK, spaCy, TF-IDF, Word2Vec
Evaluation & Metrics	Precision, Recall, F1-score, RMSE, ROC-AUC
Version Control	Git & GitHub
Notebooks & IDEs	Jupyter Notebook, VS Code

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📊 Highlighted Projects

1️⃣ Insurance Premium Prediction

• Goal: Predict customer insurance premiums using demographic and risk variables.
• Approach: Regression models (Linear Regression, XGBoost, Random Forest).
• Focus: Feature selection, multicollinearity detection, and interpretability.

2️⃣ Fake or Biased Content Detection

• Goal: Classify online content as fake, biased, or neutral.
• Approach: Natural Language Processing (NLP) with metadata features.
• Focus: Text cleaning, vectorization (TF-IDF), and ensemble learning.

3️⃣ Delivery Delay Classification

• Goal: Predict delivery status — early, on time, or late.
• Approach: Multi-class classification with Logistic Regression, XGBoost, and Random Forest.
• Focus: Handling class imbalance, feature importance, and business impact analysis.

4️⃣ Agricultural Efficiency & Productivity Studies

• Goal: Analyze farm typology, post-harvest loss, and yield determinants.
• Approach: Clustering, feature selection, and supervised learning for productivity prediction.
• Focus: Data science-driven agricultural analytics.

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📚 Learning Focus Areas

• Data wrangling and cleaning workflows
• Feature engineering and transformation
• Model training, validation, and hyperparameter tuning
• Model interpretability (SHAP, feature importance)
• End-to-end data science pipeline documentation

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🧑‍💻 How to Use

1. Clone the repository

   git clone https://github.com/Tegazini/Data-Science-with-Python.git

2. Navigate into the folder

   cd Data-Science-with-Python

3. Install dependencies

   pip install -r requirements.txt

4. Run notebooks

   jupyter notebook
   

Explore each project folder for its own datasets and notebook scripts.

🌟 Future Work

• Add deep learning experiments with TensorFlow/PyTorch
• Incorporate MLOps tools (e.g., MLflow, DVC) for versioned model tracking
• Deploy selected models using Streamlit or FastAPI

🧾 Author

👤 Tega Jarikre

📧 Email: jarikretega@gmail.com

🔗 LinkedIn: https://www.linkedin.com/in/tega-jarikre-92138342

💻 GitHub: https://github.com/Tegazini

"Data science isn’t just about algorithms — it’s about understanding data deeply enough to tell meaningful stories."