https://github.com/abdiasarsene/analysis_and_findings
Librairies
https://github.com/abdiasarsene/analysis_and_findings
bert gpt missingno nltk pandas seaborn sklearn spacy statsmodels tensorflow
Last synced: 2 months ago
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Librairies
- Host: GitHub
- URL: https://github.com/abdiasarsene/analysis_and_findings
- Owner: Abdiasarsene
- Created: 2025-01-03T07:34:08.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-25T16:00:59.000Z (2 months ago)
- Last Synced: 2025-02-25T16:25:28.200Z (2 months ago)
- Topics: bert, gpt, missingno, nltk, pandas, seaborn, sklearn, spacy, statsmodels, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 11 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Real World DS - Real Data Science Projects
Welcome to the **Data Science Projects** repository! This repository contains data science projects that I have completed for real companies. These projects demonstrate my skills in data analysis, modeling, and creating analytical solutions for real business problems.
## Overview
This repository groups together various projects conducted to address specific business needs. Each project includes details on the business context, the data used, the analysis methods applied, and the results obtained. You will also find detailed notebooks, scripts, and reports for each project.
## Included Projects
1. **Sales Analysis and Forecasting**:
- **Description**: Analyzing historical sales data to identify trends and forecast future sales. Using time series models for accurate forecasting.
- **Technologies used**: Python, pandas, scikit-learn, Prophet
2. **Customer segmentation**:
- **Description**: Segment customers based on their purchasing behavior for targeted marketing campaigns. Use clustering analysis to identify distinct segments.
- **Technologies used**: Python, pandas, scikit-learn, K-means, seaborn
3. **Fraud detection**:
- **Description**: Detect fraudulent transactions using machine learning models. Analyze transaction characteristics to identify abnormal behavior.
- **Technologies used**: Python, pandas, scikit-learn, XGBoost
4. **Sentiment and emotion analysis**:
- **Description**: Analyze customer comments on social media to understand their sentiments and emotions. Use natural language processing (NLP) techniques to extract valuable insights.
- **Technologies used**: Python, NLTK, TextBlob, vaderSentiment
5. **Supply Chain Optimization**:
- **Description**: Optimize supply chains by analyzing logistics data. Identify inefficiencies and propose solutions to improve performance.
- **Technologies used**: Python, pandas, scikit-learn, PuLP
6. **Predictive Analysis of Equipment Failures**:
- **Description**: Analyze maintenance data to predict equipment failures. Use machine learning models to reduce downtime and maintenance costs.
- **Technologies used** : Python, pandas, seaborn, scikit-learn, randomForest, xgboost
## How to use this repository
1. **Clone the repository** :
```bash
git clone https://github.com/Abdiasarsene/Analysis_And_Finding