# sajal2692/data-science-portfolio architecture

> Architecture diagram of the GitHub repository sajal2692/data-science-portfolio, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-09). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/sajal2692/data-science-portfolio
- Repository: https://github.com/sajal2692/data-science-portfolio

## Overview

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/boston_housing.ipynb`](https://github.com/sajal2692/data-science-portfolio/blob/main/boston_housing/boston_housing.ipynb) (Housing Prediction), [`finding_donors/finding_donors.ipynb`](https://github.com/sajal2692/data-science-portfolio/blob/main/finding_donors/finding_donors.ipynb) (Donor Prediction), [`customer_segments/customer_segments.ipynb`](https://github.com/sajal2692/data-science-portfolio/blob/main/customer_segments/customer_segments.ipynb) (Customer Segments), [`digit_recognition-mnist-sequence.ipynb`](https://github.com/sajal2692/data-science-portfolio/blob/main/digit_recognition-mnist-sequence.ipynb) (Digit Sequences)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_ml["Machine Learning"]
  node_housing["Housing Prediction"]
  node_donors["Donor Prediction"]
  node_segments["Customer Segments"]
  node_digits["Digit Sequences"]
end

subgraph group_nlp["Language Projects"]
  node_sentiment["Tweet Sentiment"]
  node_clir["Cross-Language Search"]
  node_yelp["Yelp Classification"]
  node_sms["SMS Classification"]
end

subgraph group_analysis["Data Analysis"]
  node_titanic["Titanic Exploration"]
  node_stocks["Stock Analysis"]
  node_election["Election Polls"]
  node_calls["911 Calls"]
end

subgraph group_micro["ML Micro Projects"]
  node_knn["K-Nearest Neighbors"]
  node_svm["Support Vector Machines"]
  node_trees["Trees and Forests"]
  node_linear["Linear Regression"]
  node_logistic["Logistic Regression"]
  node_recommend["Movie Recommendations"]
end

subgraph group_visuals["Visualization Helpers"]
  node_housingvis["Housing Charts<br/>[visuals.py]"]
  node_segmentvis["Segment Charts<br/>[visuals.py]"]
  node_donorvis["Donor Charts<br/>[visuals.py]"]
end

node_user(("Notebook User"))

node_user -->|"runs analysis"| node_housing
node_user -->|"runs prediction"| node_donors
node_user -->|"runs clustering"| node_segments
node_user -->|"runs recognition"| node_digits
node_user -->|"runs classification"| node_sentiment
node_user -->|"runs retrieval"| node_clir
node_user -->|"runs classification"| node_yelp
node_user -->|"runs classification"| node_sms
node_user -->|"runs exploration"| node_titanic
node_user -->|"runs analysis"| node_stocks
node_user -->|"runs analysis"| node_election
node_user -->|"runs exploration"| node_calls
node_user -->|"runs exercise"| node_knn
node_user -->|"runs exercise"| node_svm
node_user -->|"runs exercise"| node_trees
node_user -->|"runs exercise"| node_linear
node_user -->|"runs exercise"| node_logistic
node_user -->|"runs recommendations"| node_recommend
node_housing -.->|"uses plotting helpers"| node_housingvis
node_segments -.->|"uses plotting helpers"| node_segmentvis
node_donors -.->|"uses plotting helpers"| node_donorvis

click node_housing "https://github.com/sajal2692/data-science-portfolio/blob/main/boston_housing/boston_housing.ipynb"
click node_donors "https://github.com/sajal2692/data-science-portfolio/blob/main/finding_donors/finding_donors.ipynb"
click node_segments "https://github.com/sajal2692/data-science-portfolio/blob/main/customer_segments/customer_segments.ipynb"
click node_digits "https://github.com/sajal2692/data-science-portfolio/blob/main/digit_recognition-mnist-sequence.ipynb"
click node_sentiment "https://github.com/sajal2692/data-science-portfolio/blob/main/3-Way%20Sentiment%20Analysis%20for%20Tweets.ipynb"
click node_clir "https://github.com/sajal2692/data-science-portfolio/blob/main/Cross%20Language%20Information%20Retrieval.ipynb"
click node_yelp "https://github.com/sajal2692/data-science-portfolio/blob/main/Yelp%20Review%20Classification.ipynb"
click node_sms "https://github.com/sajal2692/data-science-portfolio/blob/main/SMS%20Spam%20Classification.ipynb"
click node_titanic "https://github.com/sajal2692/data-science-portfolio/blob/main/Titanic%20Dataset%20-%20Exploratory%20Analysis.ipynb"
click node_stocks "https://github.com/sajal2692/data-science-portfolio/blob/main/Stock%20Market%20Analysis%20for%20Tech%20Stocks.ipynb"
click node_election "https://github.com/sajal2692/data-science-portfolio/blob/main/2016%20General%20Election%20Poll%20Analysis.ipynb"
click node_calls "https://github.com/sajal2692/data-science-portfolio/blob/main/911%20Calls%20-%20Exploratory%20Analysis.ipynb"
click node_knn "https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/ML%20with%20K%20Nearest%20Neighbors.ipynb"
click node_svm "https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/ML%20with%20Support%20Vector%20Machines.ipynb"
click node_trees "https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Machine%20Learning%20with%20Decision%20Trees%20and%20Random%20Forests.ipynb"
click node_linear "https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Machine%20Learning%20with%20Linear%20Regression.ipynb"
click node_logistic "https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Machine%20Learning%20with%20Logistic%20Regression.ipynb"
click node_recommend "https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Recommender%20Systems%20with%20Python.ipynb"
click node_housingvis "https://github.com/sajal2692/data-science-portfolio/blob/main/boston_housing/visuals.py"
click node_segmentvis "https://github.com/sajal2692/data-science-portfolio/blob/main/customer_segments/visuals.py"
click node_donorvis "https://github.com/sajal2692/data-science-portfolio/blob/main/finding_donors/visuals.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_housing,node_donors,node_segments,node_digits,node_user toneBlue
class node_sentiment,node_clir,node_yelp,node_sms toneAmber
class node_titanic,node_stocks,node_election,node_calls toneMint
class node_knn,node_svm,node_trees,node_linear,node_logistic,node_recommend toneRose
class node_housingvis,node_segmentvis,node_donorvis toneIndigo
```

## Components

### Machine Learning

- [Housing Prediction](https://github.com/sajal2692/data-science-portfolio/blob/main/boston_housing/boston_housing.ipynb): `boston_housing/boston_housing.ipynb`
- [Donor Prediction](https://github.com/sajal2692/data-science-portfolio/blob/main/finding_donors/finding_donors.ipynb): `finding_donors/finding_donors.ipynb`
- [Customer Segments](https://github.com/sajal2692/data-science-portfolio/blob/main/customer_segments/customer_segments.ipynb): `customer_segments/customer_segments.ipynb`
- [Digit Sequences](https://github.com/sajal2692/data-science-portfolio/blob/main/digit_recognition-mnist-sequence.ipynb): `digit_recognition-mnist-sequence.ipynb`

### Language Projects

- [Tweet Sentiment](https://github.com/sajal2692/data-science-portfolio/blob/main/3-Way%20Sentiment%20Analysis%20for%20Tweets.ipynb): `3-Way Sentiment Analysis for Tweets.ipynb`
- [Cross-Language Search](https://github.com/sajal2692/data-science-portfolio/blob/main/Cross%20Language%20Information%20Retrieval.ipynb): `Cross Language Information Retrieval.ipynb`
- [Yelp Classification](https://github.com/sajal2692/data-science-portfolio/blob/main/Yelp%20Review%20Classification.ipynb): `Yelp Review Classification.ipynb`
- [SMS Classification](https://github.com/sajal2692/data-science-portfolio/blob/main/SMS%20Spam%20Classification.ipynb): `SMS Spam Classification.ipynb`

### Data Analysis

- [Titanic Exploration](https://github.com/sajal2692/data-science-portfolio/blob/main/Titanic%20Dataset%20-%20Exploratory%20Analysis.ipynb): `Titanic Dataset - Exploratory Analysis.ipynb`
- [Stock Analysis](https://github.com/sajal2692/data-science-portfolio/blob/main/Stock%20Market%20Analysis%20for%20Tech%20Stocks.ipynb): `Stock Market Analysis for Tech Stocks.ipynb`
- [Election Polls](https://github.com/sajal2692/data-science-portfolio/blob/main/2016%20General%20Election%20Poll%20Analysis.ipynb): `2016 General Election Poll Analysis.ipynb`
- [911 Calls](https://github.com/sajal2692/data-science-portfolio/blob/main/911%20Calls%20-%20Exploratory%20Analysis.ipynb): `911 Calls - Exploratory Analysis.ipynb`

### ML Micro Projects

- [K-Nearest Neighbors](https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/ML%20with%20K%20Nearest%20Neighbors.ipynb): `ML Micro Projects/ML with K Nearest Neighbors.ipynb`
- [Support Vector Machines](https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/ML%20with%20Support%20Vector%20Machines.ipynb): `ML Micro Projects/ML with Support Vector Machines.ipynb`
- [Trees and Forests](https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Machine%20Learning%20with%20Decision%20Trees%20and%20Random%20Forests.ipynb): `ML Micro Projects/Machine Learning with Decision Trees and Random Forests.ipynb`
- [Linear Regression](https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Machine%20Learning%20with%20Linear%20Regression.ipynb): `ML Micro Projects/Machine Learning with Linear Regression.ipynb`
- [Logistic Regression](https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Machine%20Learning%20with%20Logistic%20Regression.ipynb): `ML Micro Projects/Machine Learning with Logistic Regression.ipynb`
- [Movie Recommendations](https://github.com/sajal2692/data-science-portfolio/blob/main/ML%20Micro%20Projects/Recommender%20Systems%20with%20Python.ipynb): `ML Micro Projects/Recommender Systems with Python.ipynb`

### Visualization Helpers

- [Housing Charts](https://github.com/sajal2692/data-science-portfolio/blob/main/boston_housing/visuals.py): `boston_housing/visuals.py`
- [Segment Charts](https://github.com/sajal2692/data-science-portfolio/blob/main/customer_segments/visuals.py): `customer_segments/visuals.py`
- [Donor Charts](https://github.com/sajal2692/data-science-portfolio/blob/main/finding_donors/visuals.py): `finding_donors/visuals.py`

### Other components

- **Notebook User**

## Connections

- Notebook User → Housing Prediction: runs analysis (evidence: `readme.md`)
- Notebook User → Donor Prediction: runs prediction (evidence: `readme.md`)
- Notebook User → Customer Segments: runs clustering (evidence: `readme.md`)
- Notebook User → Digit Sequences: runs recognition (evidence: `readme.md`)
- Notebook User → Tweet Sentiment: runs classification (evidence: `readme.md`)
- Notebook User → Cross-Language Search: runs retrieval (evidence: `readme.md`)
- Notebook User → Yelp Classification: runs classification (evidence: `readme.md`)
- Notebook User → SMS Classification: runs classification (evidence: `readme.md`)
- Notebook User → Titanic Exploration: runs exploration (evidence: `readme.md`)
- Notebook User → Stock Analysis: runs analysis (evidence: `readme.md`)
- Notebook User → Election Polls: runs analysis (evidence: `readme.md`)
- Notebook User → 911 Calls: runs exploration (evidence: `readme.md`)
- Notebook User → K-Nearest Neighbors: runs exercise (evidence: `readme.md`)
- Notebook User → Support Vector Machines: runs exercise (evidence: `readme.md`)
- Notebook User → Trees and Forests: runs exercise (evidence: `readme.md`)
- Notebook User → Linear Regression: runs exercise (evidence: `readme.md`)
- Notebook User → Logistic Regression: runs exercise (evidence: `readme.md`)
- Notebook User → Movie Recommendations: runs recommendations (evidence: `readme.md`)
- Housing Prediction → Housing Charts: uses plotting helpers
- Customer Segments → Segment Charts: uses plotting helpers
- Donor Prediction → Donor Charts: uses plotting helpers