- This is a portfolio of my ongoing and completed data science projects. The table also shows my technical skills and tools used for the projects.
- The portfolio contains links to current projects in my portfolio and shows my hands-on experience on different areas in Data Science and Machine Learning.
- Languages: Python, PostgreSQL, Microsoft SQL Server, R.
- Machine Learning: Regression, Classification, Clustering, Logistic Regression, Random Forest, SVM, NLP, Neural Networks, Time Series Analysis, Computer Vision, Deep Learning.
- Tools: NumPy, Pandas, SciPy, Scikit-learn, TensorFlow, Keras, Spark, Matplotlib, Seaborn, Plotly, PyTorch, XGBoost, CatBoost, AdaBoost.
- Frameworks:
No. | Hands-on Experience | Title | Project | Technical skills | Completed |
---|---|---|---|---|---|
1 | Data Preprocessing | Credit Rating Analytics | Analyzing borrowers’ risk of defaulting | Python, NLTK, WordNetLemmatizer, SnowballStemmer, Seaborn, Matplotlib | ☑ |
2 | Data Wrangling in R | Investigating the Coronavirus Pandemic | Covid-19 Data Manipulation in R | R, dplyr, readr, tidyr, ggplot2 | ☐ |
3 | Exploratory Data Analysis (EDA) | Car Sales Analytics | Research on car sales ads | Python, Seaborn, Matplotlib | ☑ |
4 | Statistical Data Analysis (SDA) | Telecom prepaid plan analysis | Statistical Data Analysis on Real Telecom Data | Python, Numpy, SciPy, Seaborn, Matplotlib | ☑ |
5 | Data Visualization and Storytelling with Data | Online GameStore Analytics | Data Visualization and Storytelling for an Online GameStore | Python, Pandas, Squarify, Seaborn, Matplotlib | ☑ |
6 | Webscraping and Data Storage in databases | Ride-sharing analytics | Data Collection, Webscraping and Storage for a new ride-sharing company | PostgreSQL, Python, BeautifulSoup, Seaborn, Matplotlib | ☐ |
7 | Real Data and Webscraping | Web Scraping Data Scientist job openings with BeautifulSoup and Requests | Web Scraping with BeautifulSoup and Requests | Python, BeautifulSoup, Requests | ☑ |
8 | Databases and SQL data analysis | Chicago City data analysis | Analyzing Chicago City data with SQL and Python | PostgreSQL, Python | ☑ |
9 | Machine Learning - Classification System | Machine Learning for phone plan recommendation system | Phone plan recommendation system | Python, Scikit-learn, Pandas | ☑ |
10 | Supervised Learning - Prediction | Bank Customer Churn Prediction | Bank Customer Churn Prediction using Machine Learning | Scikit-learn, XGBoost, GridSearchCV, AdaBoost | ☑ |
11 | Machine Learning in Business | Predicting the volume of reserves in new wells, well placements and locations optimization | Machine Learning for reservoir optimization | Python, Scikit-learn, Bootstrapping, LinearRegression | ☑ |
12 | Machine Learning in Business | Optimizing gold extraction from mined ore using Machine Learning | Machine Learning for gold extraction optimization | Python, Scikit-learn, LinearRegression | ☑ |
13 | Linear Algebra with Machine Learning | Insurance benefit prediction | Machine Learning and Linear regression for insurance benefit prediction | Scikit-learn, Linear Algebra, k-Nearest Neighbors | ☑ |
14 | Numerical Methods with Machine Learning | Used vehicle price prediction | Vehicle price prediction using various implementation of gradient boosting | Numerical Methods, CatBoost, LightGBM, XGBoost | ☑ |
15 | Time Series Analysis | Predicting number of taxi orders with Machine learning | Taxi orders prediction using Machine Learning | Time Series Analysis, CatBoost, LightGBM, XGBoost | ☑ |
16 | Natural Language Processing (NLP) | Classifying positive and negative reviews with Machine Learning for Texts | IMBD Movie Sentiment Analysis using NLP | SGDClassifier, Naïve bayes, LightGBM, spaCy, TF-IDF, BERT | ☑ |
17 | Computer Vision (CV) | Predicting the approximate age of a person from a photograph | Customer Image Processing using deep learning | Tensorflow | ☑ |
18 | Machine Learning for Prediction | Telecom Client Churn Forecasting | Telecom Client Churn Forecasting using Machine Learning | Machine learning algorithms, XGBoost, CatBoost, LightGBM | ☑ |