https://github.com/jmcheon/dslr
Subject created by the 42AI association. Discover Data Science in the projects where you re-constitute Poudlard’s Sorting Hat. Warning: this is not a subject on cameras.
https://github.com/jmcheon/dslr
data-science logistic-regression python3
Last synced: about 2 months ago
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Subject created by the 42AI association. Discover Data Science in the projects where you re-constitute Poudlard’s Sorting Hat. Warning: this is not a subject on cameras.
- Host: GitHub
- URL: https://github.com/jmcheon/dslr
- Owner: jmcheon
- Created: 2023-06-04T19:34:34.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-05T11:59:44.000Z (over 2 years ago)
- Last Synced: 2025-01-19T20:22:20.279Z (over 1 year ago)
- Topics: data-science, logistic-regression, python3
- Language: Python
- Homepage:
- Size: 178 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Datascience X Logistic Regression - Harry Potter and a Data Scientist
>*_Summary: Write a classifier and save Hogwarts!_*
| Requirements | Skills |
|--------------|--------|
| - `python3.11`
- `numpy`
- `pandas`
- `matplotlib`
- `scikit-learn`
- `seaborn` | - `DB & Data`
- `Algorithms & AI` |
## 0. Installation
```bash
sh ./launch_conda.sh
conda activate 42AI-${USER}
```
## 1. Data Analysis
### Description
| Column name | data type |
|:------------------------------|:------------:|
| Index | int |
| Hogwarts House | object (str) |
| First Name | object (str) |
| Last Name | object (str) |
| Birthday | object (str) |
| Best Hand | object (str) |
| Arithmancy | float |
| Astronomy | float |
| Herbology | float |
| Defense Against the Dark Arts | float |
| Divination | float |
| Muggle Studies | float |
| Ancient Runes | float |
| History of Magic | float |
| Transfiguration | float |
| Potions | float |
| Care of Magical Creatures | float |
| Charms | float |
| Flying | float |
##### Categorical data
```
Index 0 1 2 3 ... 1596 1597 1598 1599
Hogwarts House Ravenclaw Slytherin Ravenclaw Gryffindor ... Slytherin Gryffindor Hufflepuff Hufflepuff
First Name Tamara Erich Stephany Vesta ... Shelli Benjamin Charlotte Kylie
Last Name Hsu Paredes Braun Mcmichael ... Lock Christensen Dillon Nowak
Birthday 2000-03-30 1999-10-14 1999-11-03 2000-08-19 ... 1998-03-12 1999-10-24 2001-09-21 2000-08-21
Best Hand Left Right Left Left ... Left Right Left Left
```
##### Numerical data
```
count mean std min 25% 50% 75% max
Arithmancy 1566.0 49634.570243 16679.806036 -24370.000000 38511.500000 49013.500000 60811.250000 104956.000000
Astronomy 1568.0 39.797131 520.298268 -966.740546 -489.551387 260.289446 524.771949 1016.211940
Herbology 1567.0 1.141020 5.219682 -10.295663 -4.308182 3.469012 5.419183 11.612895
Defense Against the Dark Arts 1569.0 -0.387863 5.212794 -10.162119 -5.259095 -2.589342 4.904680 9.667405
Divination 1561.0 3.153910 4.155301 -8.727000 3.099000 4.624000 5.667000 10.032000
Muggle Studies 1565.0 -224.589915 486.344840 -1086.496835 -577.580096 -419.164294 254.994857 1092.388611
Ancient Runes 1565.0 495.747970 106.285165 283.869609 397.511047 463.918305 597.492230 745.396220
History of Magic 1557.0 2.963095 4.425775 -8.858993 2.218653 4.378176 5.825242 11.889713
Transfiguration 1566.0 1030.096946 44.125116 906.627320 1026.209993 1045.506996 1058.436410 1098.958201
Potions 1570.0 5.950373 3.147854 -4.697484 3.646785 5.874837 8.248173 13.536762
Care of Magical Creatures 1560.0 -0.053427 0.971457 -3.313676 -0.671606 -0.044811 0.589919 3.056546
Charms 1600.0 -243.374409 8.783640 -261.048920 -250.652600 -244.867765 -232.552305 -225.428140
Flying 1600.0 21.958012 97.631602 -181.470000 -41.870000 -2.515000 50.560000 279.070000
```
## 2. Data Visualization
|[histogram.py](./histogram.py)|[scatter.py](./scatter.py) |
|---------------------------------------------|-------------------------------------------------------|
|||
### 2.1 Histogram
Make a script called histogram.[extension] which displays a histogram answering the next question :
Which Hogwarts course has a homogeneous score distribution between all four houses?
```
variances:
Arithmancy 2.782159e+08
Astronomy 2.707103e+05
Herbology 2.724508e+01
Defense Against the Dark Arts 2.717322e+01
Divination 1.726653e+01
Muggle Studies 2.365313e+05
Ancient Runes 1.129654e+04
History of Magic 1.958748e+01
Transfiguration 1.947026e+03
Potions 9.908986e+00
Care of Magical Creatures 9.437286e-01
Charms 7.715233e+01
Flying 9.531930e+03
dtype: float64
```
### 2.2 Scatter plot
Make a script called scatter_plot.[extension] which displays a scatter plot answering the next question :
What are the two features that are similar?
```
Feature 1 Feature 2 Threshold
Astronomy Defense Against the Dark Arts 0.9999999999999984
Muggle Studies Charms 0.8476070313934801
History of Magic Transfiguration 0.8492027176461879
History of Magic Flying 0.8962834248882747
Transfiguration Flying 0.8736726050021425
```
### 2.3 Pair plot
Make a script called pair_plot.[extension] which displays a pair plot or scatter plot matrix (according to the library that you are using).
From this visualization, what features are you going to use for your logistic regression?
## 3. Logistic Regression
#### What is one-vs-all method
The one-vs-all (OvA) method, also known as one-vs-rest (OvR), is a strategy used in multi-class classification problems in machine learning. It involves training multiple binary classifiers, one for each class, to distinguish between that specific class and all the other classes combined.
#### Logistic regression
Logistic regression is a supervised machine learning algorithm used primarily for binary classification problems. Its goal is to predict one of two possible outcomes (usually represented as 0 and 1), based on the given input features.
It is a type of generalized linear model (GLM) that uses a logistic function (sigmoid function) to model the probability of an instance belonging to the positive class, given its features.
- [logreg_train.py](./logreg_train.py) saves ./weights.csv
```
Usage: python logreg_train.py [data path] (for batch gradient descent)
python logreg_train.py [data path] [batch option]
three batch options: batch, sgd (for stochastic), mini (for mini-batch)
```
- [logreg_predict.py](./logreg_predict.py) takes ./weights.csv and saves ./houses.csv
- [evaluate.py](./evaluate.py) - evaluates on dataset_truth.csv with houses.csv
Stochastic
Mini batch
Batch
