@@ -4,7 +4,7 @@ This project aims to develop data analysis and recommendation tools.
This README outlines the structure and summarizes our project.
## Python Libraries :
# Python Libraries :
To run the various scripts and notebooks, ensure that you have installed the following Python libraries:
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@@ -21,10 +21,10 @@ pip install streamlit
pip install requests
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## Project Structure
# Project Structure
The project is organized into the following key components:
### Configuration
## Configuration
1.***configs.py***
- Defines an `EvalConfig` class for storing configurations for evaluating multiple recommendation models.
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@@ -56,16 +56,16 @@ The project is organized into the following key components:
- Recommendation algorithms are defined as classes inheriting from Surprise's `AlgoBase` class, each implementing an `estimate` method to predict user ratings for items.
### Backend folder
# Backend folder
This folder contains all the data, jupyter notebook, scripts python used to improve user experience.
#### Analytics
## Analytics
***analytics_small.ipynb***
- Performs data analysis to understand the datasets and their properties.
- Analyzes the number of ratings, unique users, unique items, and distribution of ratings.
#### Evaluation
## Evaluation
***evaluator.ipynb***
- Evaluates different recommendation models using various cross-validation techniques.
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@@ -81,7 +81,7 @@ This folder contains all the data, jupyter notebook, scripts python used to impr
- Exports the evaluation report to a CSV file.
#### Content Based
## Content Based
***hackathon_make_predictions.ipynb***
Define a function make_hackathon_prediction that takes feature_method and regressor_method as input.
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@@ -124,7 +124,7 @@ The system supports the following regression models for predicting user ratings:
### Datasets
# Datasets
1.***small***
- Contains traditional MovieLens data and is used for evaluating models and building our recommendation system.
