semvec/pypi-packages huggingface dataset:
101703311-OUTLIERS 1.0.2
A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Requires Python: >=3.6

# Outlier Removal Using InterQuartile Range

**Project 2 : UCS633**


Submitted By: **Lokesh Arora 101703311**

***
pypi: <https://pypi.org/project/101703311_OUTLIERS/>
***

## InterQuartile Range (IQR) Description

Any set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:

The minimum or lowest value of the dataset.
<br>
The first quartile Q1, which represents a quarter of the way through the list of all data.
<br>
The median of the data set, which represents the midpoint of the whole list of data.
<br>
The third quartile Q3, which represents three-quarters of the way through the list of all data.
<br>
The maximum or highest value of the data set.
<br>
<br>
These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.

## Calculation of IQR

IQR = Q3 – Q1
<br>
MIN = Q1 - (1.5*IQR)
<br>
MAX = Q3 + (1.5*IQR)
<br>

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install 101703311_OUTLIERS.

```bash
pip install 101703311_OUTLIERS
```
<br>

## How to use this package:

101703311_OUTLIERS can be run as shown below:


### In Command Prompt
```
>> outlierRemoval dataset.csv
```
<br>


## Sample dataset

Marks | Students 
:------------: | :-------------:
3 | Student1
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7
99 | Student8
1 | Student9

<br>


## Output Dataset after Removal

Marks | Students 
:------------: | :-------------:
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7

<br>

It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.


## License
[MIT](https://choosealicense.com/licenses/mit/)
101703312-outlierRemoval 1.0.0
A python package for removing outliers from a dataset using InterQuartile Range (IQR)
Requires Python: >=3.6

# Outlier Removal Using InterQuartile Range

**Project 2 : UCS633**


Submitted By: **Lovish Jindal 101703312**

***
pypi: <https://pypi.org/project/101703312_outlierRemoval/>
***

## InterQuartile Range (IQR) Description

Any set of data can be described by its five-number summary. These five numbers, which give you the information you need to find patterns and outliers, consist of:

The minimum or lowest value of the dataset.
<br>
The first quartile Q1, which represents a quarter of the way through the list of all data.
<br>
The median of the data set, which represents the midpoint of the whole list of data.
<br>
The third quartile Q3, which represents three-quarters of the way through the list of all data.
<br>
The maximum or highest value of the data set.
<br>
<br>
These five numbers tell a person more about their data than looking at the numbers all at once could, or at least make this much easier.

## Calculation of IQR

IQR = Q3 – Q1
<br>
MIN = Q1 - (1.5*IQR)
<br>
MAX = Q3 + (1.5*IQR)
<br>

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install 101703312_outlierRemoval.

```bash
pip install 101703312_outlierRemoval
```
<br>

## How to use this package:

101703312_outlierRemoval can be run as shown below:


### In Command Prompt
```
>> outlierRemoval dataset.csv
```
<br>


## Sample dataset

Marks | Students 
:------------: | :-------------:
3 | Student1
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7
99 | Student8
1 | Student9

<br>


## Output Dataset after Removal

Marks | Students 
:------------: | :-------------:
57 | Student2
65 | Student3
98 | Student4
43 | Student5
44 | Student6
54 | Student7

<br>

It is clearly visible that the rows containing Student1, Student8 and Student9 have been removed due to them being Outliers.


## License
[MIT](https://choosealicense.com/licenses/mit/)
gym-bandits 0.0.1
Implements multi-armed bandits

# Gym Bandits

A multi-armed bandits environment for OpenAI gym.

## Installation instructions

Requirements: gym and numpy

```
pip install gym-bandits
```

## Usage
```
import gym
import gym_bandits

env = gym.make('MultiArmedBandits-v0') # 10-armed bandit
env = gym.make('MultiArmedBandits-v0', nr_arms=15) # 15-armed bandit
```
Outlier-Removal-101703571 0.0.1
Python package for Outlier Removal using Row Removal method.
Requires Python: >=3.6

﻿# Outlier Removal Package

In statistics, an outlier is an observation point that is distant from other observations. The outliers can be a result of a mistake during data collection or it can be just an indication of variance in your data. The package uses interquartile range method to detect and remove outliers from a csv file. 


## Installation

$ python3 Outlier_Removal_101703571.py filename
> Link for installation has also been provided above
linalg-simple 0.0.3
Simple Linear Algebra Package
Requires Python: >=3.6

# linalg_simple

_A simple linear algebra package written in Python 3_

This is a more or less direct implementation of the **Udacity** [Linear Algebra Refresher Course](https://www.udacity.com/course/linear-algebra-refresher-course--ud953).

---

## Installation

**linalg_simple** is available from the Python Package Index [PyPi](https://pypi.org).
If you have Python 3 installed on your system you can use the `pip install` command:

```
pip install linalg_simple
```

---

## Classes

The linalg_simple package currently offers the `Vector()` class, which can directly be imported into your project with:

```python
from linalg import Vector
```

---

## Vector Class

### _Attributes_

The `Vector()` class has two attributes:

- `Vector.coordinates` (Decimal object)
- `Vector.dimension` (int)

### _Methods_

The following `Vector()` class methods are available:

### Constructor

Arguments: int, float

```
v1 = Vector([1.5, -4.8, 3])
```

### + Method

```
v3 = v1 + v2
```

### - Method

```
v3 = v1 - v2
```

### Scalar Mutliplication

Arguments: int, float

```
v1.times_scalar(42)
```

### Magnitude

```
vector_length = v1.magnitude()
```

### Vector Normalization

```
unit_vector = v1.normalized()
```

### Dot Product

Arguments: vector object

```
dot_product = v1.dot(v2)
```

### Angle between two vectors

Arguments: vector object

```
angle = v1.angle_with(v2)
```

### Check if vector's length is zero

To avoid precision errors, you can indicate a tolerance (default: 1e-10).

```
v1.is_zero()
```

### Check if two vectors are orthogonal to each other

To avoid precision errors, you can indicate a tolerance (default: 1e-10).

Arguments: vector object, tolerance

```
v1.is_orthogonal_to(v2)
```

### Check if two vectors are parallel to each other

Arguments: vector object

```
v1.is_parallel_to(v2)
```

### Calculate projection of a vector onto a basis vector

Arguments: vector object (basis vector)

```
projection_vector = v1.component_parallel_to(v2)
```

### Calculate orthogonal component of vector

Arguments: vector object (basis vector)

```
projection_vector = v1.component_orthogonal_to(v2)
```

### Calculate Cross Product of two vectors

The vectors should be 3D (2D vectors will be augmented to 3D).

Arguments: vector object

```
crossproduct_vector = v1.cross(v2)
```

### Calculate the parallelogram spanned by two vectors

Arguments: vector object

```
area = v1.area_of_parallelogram_with(v2)
```

### Calculate area of a parallelogram spanned by two vectors

Arguments: vector object

```
area = v1.area_of_parallelogram_with(v2)
```

### Calculate area of a trinagle spanned by two vectors

Arguments: vector object

```
area = v1.area_of_triangle_with(v2)
```
topsis-analysis 0.0.3
topsis analysis of csv file
Requires Python: >=3.6

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

This package will helps you in TOPSIS analysis of csv files.
Please make sure that your dataset doesn't contain categorical data.

Source:
https://en.wikipedia.org/wiki/TOPSIS