How sajal2692/data-science-portfolio works
This repository is a portfolio of independent, executable Jupyter data-science projects rather than a single application. A user runs a notebook, which performs a project-specific analysis or modeling task and presents its results. The README documents supervised, unsupervised, deep-learning, NLP, exploratory-analysis, and micro-project capabilities; the graph groups representative notebooks by domain. Source excerpts cover only visualization helpers, so notebook-internal calls and most dataset wiring are not shown; edges therefore reflect documented capabilities rather than inferred code-level pipelines.
Main entry points: boston_housing.ipynb Housing Prediction · finding_donors.ipynb Donor Prediction · customer_segments.ipynb Customer Segments · digit_recognition-mnist-sequence.ipynb Digit Sequences
Components 22
Machine Learning
- Housing Prediction
boston_housing/boston_housing.ipynb - Donor Prediction
finding_donors/finding_donors.ipynb - Customer Segments
customer_segments/customer_segments.ipynb - Digit Sequences
digit_recognition-mnist-sequence.ipynb
Language Projects
- Tweet Sentiment
3-Way Sentiment Analysis for Tweets.ipynb - Cross-Language Search
Cross Language Information Retrieval.ipynb - Yelp Classification
Yelp Review Classification.ipynb - SMS Classification
SMS Spam Classification.ipynb
Data Analysis
- Titanic Exploration
Titanic Dataset - Exploratory Analysis.ipynb - Stock Analysis
Stock Market Analysis for Tech Stocks.ipynb - Election Polls
2016 General Election Poll Analysis.ipynb - 911 Calls
911 Calls - Exploratory Analysis.ipynb
ML Micro Projects
- K-Nearest Neighbors
ML Micro Projects/ML with K Nearest Neighbors.ipynb - Support Vector Machines
ML Micro Projects/ML with Support Vector Machines.ipynb - Trees and Forests
ML Micro Projects/Machine Learning with Decision Trees and Random Forests.ipynb - Linear Regression
ML Micro Projects/Machine Learning with Linear Regression.ipynb - Logistic Regression
ML Micro Projects/Machine Learning with Logistic Regression.ipynb - Movie Recommendations
ML Micro Projects/Recommender Systems with Python.ipynb
Visualization Helpers
- Housing Charts
boston_housing/visuals.py - Segment Charts
customer_segments/visuals.py - Donor Charts
finding_donors/visuals.py
Other components
- Notebook User
Connections 21
- Notebook User → Housing Prediction: runs analysis
readme.md - Notebook User → Donor Prediction: runs prediction
readme.md - Notebook User → Customer Segments: runs clustering
readme.md - Notebook User → Digit Sequences: runs recognition
readme.md - Notebook User → Tweet Sentiment: runs classification
readme.md - Notebook User → Cross-Language Search: runs retrieval
readme.md - Notebook User → Yelp Classification: runs classification
readme.md - Notebook User → SMS Classification: runs classification
readme.md - Notebook User → Titanic Exploration: runs exploration
readme.md - Notebook User → Stock Analysis: runs analysis
readme.md - Notebook User → Election Polls: runs analysis
readme.md - Notebook User → 911 Calls: runs exploration
readme.md - Notebook User → K-Nearest Neighbors: runs exercise
readme.md - Notebook User → Support Vector Machines: runs exercise
readme.md - Notebook User → Trees and Forests: runs exercise
readme.md - Notebook User → Linear Regression: runs exercise
readme.md - Notebook User → Logistic Regression: runs exercise
readme.md - Notebook User → Movie Recommendations: runs recommendations
readme.md - Housing Prediction → Housing Charts: uses plotting helpers
- Customer Segments → Segment Charts: uses plotting helpers
- Donor Prediction → Donor Charts: uses plotting helpers