shrinath305 / data-science-portfolio

Portfolio of data science projects completed for academic, self learning, and hobby purposes.

Data Science Portfolio

This repository containing portfolio of all data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks, and R markdown files.

Content

• Machine learning
  • Bicycle Prediction: A model to determine the extent to which weather and seasonal factors—temperature, precipitation, and daylight hours—affect the volume of bicycle traffic through this corridor utilizing machine learning.
  • Market-Basket-analysis: Mine frequent itemsets, association rules or association hyperedges using the Apriori algorithm. The Apriori algorithm employs level-wise search for frequent item sets.
  • Supervised Learning(Classification): Testing out several different supervised clssfication algorithms to build a model that accurately predicts whether an individual has Breast cancer or not.
  • Deep Learning: Photo Recognition using CNNs: Designing and implementing a Convolutional Neural Network that learns to recognize the image.

Tools: scikit-learn, Pandas, Seaborn, Matplotlib, R studio, R Markdown

Natural Language Processing

• Yelp review analysis: Build model which read alll review and whether the review was good or bad.

• Spam message detection : A model to determine whether the upcoming mail is Spam or not.

Tools : NLTK, scikit

Data Analysis and Visualisation

• Analysis-on-US-census-data: Analysis of walkability of suburbs in Melbourne, Victoria and its implications.
• Titanic Dataset - Exploratory Analysis: Exploratory Analysis of the passengers onboard RMS Titanic using Pandas and Seaborn visualisations.
• Big-mart-sales-Prediction: To predict the sales based on Purchase history, Store location and many more variables .
• correlation between corruption and development: Comparing the corruption index with the UN's Human Development Index (a measure combining health, wealth and education).
• 911 Calls - Exploratory Analysis: Exploratory Data Analysis of the 911 calls dataset hosted on Kaggle. Demonstrates extraction of useful features from different variables.

Micro Projects:

• ML with Logistic Regression: Using Logistic Regression to predict the quality of wine.
• ML with Decision Trees and Random Forests: Using Decision Trees and Random Forests to predict whether a lender will pay their loan back. Uses publically available data from LendingClub.com - Movie Recommendations using Recommender Systems: A micro project to build a recommendation system that makes movie recommendations based on user review similarities.