Python Skills Overview

The projects I’ve chosen to include here range from basic applied language, to machine learning models. Most of my projects are in the form of scripts and notebooks, fully open-sourced on my GitHub repository, Python.

A summary of some of the python packages and tasks I’m familiar with handling, can be observed below…

1. Data Manipulation & Processing

Libraries: pandas, numpy

  • Efficient in handling missing data, outliers, and feature engineering

  • Strong in data aggregation, merging, and transformation

  • Optimized large dataset operations using vectorized computations

2. Machine Learning & Predictive Modeling

Libraries: scikit-learn, statsmodels

  • Trained models like Linear Regression, Logistic Regression, Decision Trees, and Random Forests

  • Applied feature selection (LASSO, Ridge), VIF checks, and statistical testing

  • Optimized models with cross-validation and hyperparameter tuning

3. Unsupervised Learning & Clustering

Libraries: scikit-learn, hdbscan

  • Strong in K-Means, DBSCAN, and hierarchical clustering

  • Familiar with Principal Component Analysis (PCA) for dimensionality reduction

4. Time Series & Forecasting

Libraries: statsmodels, pmdarima, fbprophet

  • Built ARIMA, SARIMA, and exponential smoothing models

  • Used autocorrelation and differencing for trend analysis

5. Natural Language Processing (NLP)

Libraries: nltk, spacy, transformers

  • Performed tokenization, stemming, lemmatization, stopword removal

  • Built TF-IDF, BERT, and sentiment analysis models

6. Data Engineering & Big Data

Libraries: sqlalchemy, pyspark, dask

  • Built ETL pipelines for large-scale data processing

  • Optimized SQL queries using Python (SQLAlchemy)

7. Data Visualization

Libraries: matplotlib, seaborn, plotly, folium

  • Created interactive and static visualizations

  • Built geospatial visualizations using Folium & GeoPandas

8. Model Evaluation & Optimization

Libraries: scikit-learn, shap, lime

  • Evaluated models using ROC-AUC, confusion matrices, precision-recall

  • Explained complex models with SHAP & LIME

9. Automation & Scripting

  • Automated data workflows with Python scripts & Jupyter Notebooks

  • Built Python-based task schedulers and reporting tools

Next

Predictive Modeling