https://github.com/sumansuhag/prediction_model

This repository features a collection of Jupyter notebooks designed to showcase the practical applications of machine learning, data preprocessing, feature engineering, and recommendation systems. These notebooks enable users to explore, analyze, and predict business events.
https://github.com/sumansuhag/prediction_model

algotithms artificial-intelligence data logistic-regression machine-learning-algorithms science sckiit-learn

Last synced: about 2 months ago
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This repository features a collection of Jupyter notebooks designed to showcase the practical applications of machine learning, data preprocessing, feature engineering, and recommendation systems. These notebooks enable users to explore, analyze, and predict business events.

• Host: GitHub
• URL: https://github.com/sumansuhag/prediction_model
• Owner: sumansuhag
• License: mit
• Created: 2025-01-09T16:31:49.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-01-24T07:39:20.000Z (4 months ago)
• Last Synced: 2025-01-24T08:27:48.546Z (4 months ago)
• Topics: algotithms, artificial-intelligence, data, logistic-regression, machine-learning-algorithms, science, sckiit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 377 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Machine learning prediction_model

events_dataset_Analyzer.ipynb:

Analyzes event data through preprocessing, feature engineering, and machine learning. Explores relationships between event attributes and builds predictive models.

Business_Recommendation.ipynb:

Develops a business recommendation system using user and product data. Employs machine learning to provide personalized suggestions and improve engagement.

Feature__Extraction_Dashboard.ipynb:

Creates an interactive dashboard for feature extraction, allowing users to visualize and manipulate data for machine learning models.

# Data Analysis and Recommendation System Projects

This repository contains Jupyter notebooks focused on different aspects of data analysis, machine learning, and business recommendation systems. Each notebook demonstrates practical applications of data science techniques such as data preprocessing, feature engineering, model training, and performance evaluation.

`events_dataset_Analyzer.ipynb

- **Description**: Analyzes event data, including data preprocessing, feature engineering, and machine learning models. The notebook explores relationships between various event attributes and predicts the target variable based on historical data.

- **Key Features**:

  - Data Cleaning & Missing Value Handling

  - Feature Engineering

  - Model Evaluation and Accuracy Testing

 `Business_Recommendation.ipynb

- **Description**: Develops a personalized business recommendation system. This system suggests products or services to users based on their past interactions, using machine learning algorithms.

- **Key Features**:

  - Data Preparation for Recommendation Systems

  - Machine Learning-based Recommendations

  - Performance Evaluation

`Feature__Extraction_Dashboard.ipynb

- **Description**: Creates an interactive dashboard for feature extraction, helping users visualize and manipulate key features of a dataset for further analysis or machine learning modeling.

Key Features:

  - Interactive Data Exploration

  - Feature Scaling, Selection, and Transformation

  - Real-time Visualization and Manipulation

## Prerequisites

To run the notebooks, make sure you have the following dependencies installed:

- Python 3.x

- pandas

- numpy

- scikit-learn

- xgboost

- imbalanced-learn

- polars

- matplotlib

- seaborn

You can install the dependencies using `pip`:

```bash

pip install pandas numpy scikit-learn xgboost imbalanced-learn polars matplotlib seaborn