https://github.com/ibrahimm7004/machine-learning-projects
A collection of my ML projects.
https://github.com/ibrahimm7004/machine-learning-projects
ai artificial-intelligence data-analysis data-science llm machine-learning ml nlp python sklearn tensorflow
Last synced: 3 months ago
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A collection of my ML projects.
- Host: GitHub
- URL: https://github.com/ibrahimm7004/machine-learning-projects
- Owner: ibrahimm7004
- Created: 2025-02-17T21:02:40.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-21T19:43:03.000Z (over 1 year ago)
- Last Synced: 2025-03-21T20:30:29.692Z (over 1 year ago)
- Topics: ai, artificial-intelligence, data-analysis, data-science, llm, machine-learning, ml, nlp, python, sklearn, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 5.75 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: news-sentiment-analysis/app.py
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README
# 🚀 Machine Learning Projects Repository
This repository contains a collection of **Machine Learning projects**, covering various domains such as **fraud detection, financial sentiment analysis, and more**. Each project is self-contained, demonstrating a specific ML/AI concept with clear implementations and results.
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## 🛡️ Credit Card Fraud Detection
#### Overview
This project implements a **credit card fraud detection system** using **Support Vector Machines (SVM)** and **Principal Component Analysis (PCA)**. The model analyzes financial transactions to classify them as **fraudulent or authentic**, helping mitigate risks in digital financial systems.
#### 🛠️ Technologies Used
- **Machine Learning:** SVM, PCA
- **Libraries:** Scikit-learn, NumPy, Pandas, Matplotlib
- **Dataset:** Financial transaction records with fraud labels
#### 🔑 Key Features
- **Dimensionality Reduction:** PCA improves model efficiency.
- **Fraud Classification:** SVM handles high-dimensional transaction data.
- **Data Preprocessing:** Balanced dataset using oversampling for better fraud detection.
#### 📊 Hierarchical Fraud Classification
Fraudsters are categorized based on their corporate and community level roles.
#### 🔍 Principal Component Analysis (PCA)
To reduce dimensionality, PCA was applied, and the scree plot below shows the eigenvalues of each principal component.
#### ⚡ ML Pipeline
Our pipeline standardizes the data, applies PCA for feature selection, and then uses an **SVM classifier** to predict fraudulent transactions.
#### 🚀 Results
- **Model Accuracy:** **72.63% (test), 72.93% (train)**
- **Fraudulent Transactions Identified:** Mainly found in **Transfer & Cash-Out transactions**.
- **Dimensionality Reduction Success:** PCA helped optimize performance while retaining fraud detection accuracy.
🔗 **[Full Report & Code](fraud-detection/)**
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## 💰 Financial News Sentiment Analysis Application
#### 📌 Overview
This project implements a **news sentiment analysis application** using the **DistilRoBERTa model fine-tuned for financial news sentiment analysis**, accessible via the **Hugging Face API**. The model classifies financial texts, such as market reports and news articles, into different sentiment categories to help users analyze the market sentiment.
#### 🛠️ Technologies Used
- **Machine Learning:** DistilRoBERTa (fine-tuned for financial sentiment analysis)
- **Libraries:** Hugging Face Transformers, Flask, PostgreSQL
- **Deployment:** Flask API, hosted on Heroku
#### 🔑 Key Features
- **Real-Time Sentiment Analysis:** Uses Hugging Face API for instant results.
- **Financial-Specific Model:** Trained on financial news to improve accuracy in economic contexts.
- **Web Application Interface:** Built using Flask, allowing users to input text and receive real-time analysis.
#### 🚀 How It Works
1. **User inputs financial text** (e.g., a market report or company earnings statement).
2. **The text is sent to the Hugging Face API**, which classifies sentiment as **positive, negative, or neutral**.
3. **The results are displayed** in a user-friendly interface.
#### 📈 Model Used
The **pretrained model** used for this task:
🔗 **[DistilRoBERTa fine-tuned for financial sentiment analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis)**
🔗 **[Full Code & Implementation](news-sentiment-analysis/)**
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## 💼 Credit Score Prediction
#### 📌 Overview
This project develops a **credit scoring model** using various machine learning techniques to predict an individual's creditworthiness based on financial and business data. The model leverages **XGBoost, Random Forest, Neural Networks, and other algorithms** to enhance prediction accuracy.
#### 🛠️ Technologies Used
- **Machine Learning:** XGBoost, Random Forest, Neural Networks, SVM, KNN, Linear Regression
- **Libraries:** Scikit-learn, Pandas, NumPy, Matplotlib
- **Data Processing:** One-hot encoding, Label Encoding, StandardScaler for normalization
#### 🔑 Key Features
- **Automated Data Processing:** Handles missing values, categorical data encoding, and numerical transformations.
- **Multiple Model Evaluation:** Compares various models using **MSE, RMSE, MAE, R-squared, and Adjusted R-squared**.
- **Optimal Model Selection:** Identifies the most accurate model for credit score prediction.
#### 📈 Results
- **Top Performing Model:** **XGBoost** with the highest **R-squared score of 96.78%**.
- **Feature Normalization Success:** StandardScaler helped improve model convergence and accuracy.
- **Regression Task Optimized:** Removed inappropriate evaluation metrics (e.g., F1-Score) since the task is continuous rather than classification-based.
Below is a comparison table showing the evaluation metrics for different models tested in this project:
The table highlights the accuracy of different machine learning models used for credit scoring. **XGBoost** outperforms other models with the **lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)**, indicating its high precision. Random Forest and Neural Network models also show strong performance. On the other hand, K-Nearest Neighbors (KNN) has the **highest error rates**, making it the least suitable for this task.
🔗 **[Full Report & Code](credit-scoring/)**
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## 🌾 Crop Recommendation System
#### 📌 Overview
This project develops a **Crop Recommendation System** using **Machine Learning techniques** to analyze environmental conditions like **temperature, humidity, rainfall, and soil nutrients** and suggest the best crops for cultivation.
#### 🛠️ Technologies Used
- **Machine Learning:** K-Means Clustering, SVM Classification
- **Libraries:** Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
- **Deployment:** Flask API for real-time predictions
#### 🔑 Key Features
- **Exploratory Data Analysis (EDA):**
- Provides statistical summaries and average environmental requirements for different crops.
- Identifies suitable crops for different seasons (Summer, Winter, Rainy).
- **Clustering with K-Means:**
- Determines optimal clusters using the Elbow Method.
- Groups crops based on environmental conditions and soil nutrients.
- **Crop Classification using SVM:**
- Achieves **97% accuracy** in predicting the best crop based on given environmental conditions.
- **Model Deployment:**
- Saves the trained **SVM model** as a joblib file for deployment in a **Flask environment** to make real-time predictions.
#### 🚀 How It Works
1. **Preprocesses the dataset** by standardizing environmental data.
2. **Applies K-Means clustering** to group similar crops.
3. **Trains an SVM classifier** to recommend the best crop.
4. **Deploys the trained model** via a **Flask API** for real-time crop prediction.
#### 📈 Results
- **Clustering Analysis:** Groups crops into different clusters based on environmental conditions.
- **Classification Model:** SVM model achieves **97% accuracy** in crop prediction.
- **Deployment:** The model is saved and can be used in a **Flask API** for real-world applications.
🔗 **[Full Code & Implementation](crop-recommendation-system/)**
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