recommendersystem

Recommendation System to recommend top books from the dataset

Introduction

The **recom.py** is the main program code.

The dataset is also added here under the name **data_books.csv**. Its a csv (comma separated value) file which contains 3 fields namely **Reviewer**, **Book** and **Rating** (from 1 to 10), separated by commas.

Pearson Correlation is used to find the correlation or similarity between two books. It is present in **SciPy** library under module name **stats** by name **pearsonr** function; but the main problem in this function is that it cannot handle ZeroDivisionError. So I have included my header file named **function.py** which overcome this problem.

Prerequisites

Libraries or Modules

The following **libraries** or **modules** are required to run this code and how to install them

**pandas**

pandas is a software library written for the Python programming language for data manipulation and analysis.

```
$ pip install pandas
```

**future**

future is the missing compatibility layer between Python 2 and Python 3. It allows you to use a single, clean Python 3.x-compatible codebase to support both Python 2 and Python 3 with minimal overhead.

Here, we are using**future division**statement which changes the normal division operation.

```
$ pip install future
```

**numpy**

NumPy is the fundamental package for scientific computing with Python.

```
$ pip install numpy
```

Theoretical Concept

The following **concept** is also required

**Pearson Correlation**

Pearson’s correlation coefficient is the covariance of the two variables divided by the product of their standard deviations.

OR

Pearson correlation score measures how much 2 variables change together divided by the product of how much they change individually. The more the variables change together relative to how they change individually, the higher the correlation.

It has a value between +1 and −1, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation.

Execution

The code can be run by simply writing following command in the terminal

```
$ python recom.py
```

Built with

**Python**

Author

*Vishal karur** – (https://github.com/vishalkarur)

Acknowledgments