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.
- 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
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Yelp review analysis: Build model which read alll review and whether the review was good or bad.
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Spam message detection : A model to determine whether the upcoming mail is Spam or not.
Tools : NLTK, scikit
- 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.
- 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.