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Salma0-8/README.md

🍒 Welcome to My GitHub Profile!

📊 Data Analysis and Interpretation: Expert in data cleaning, transformation, and exploratory data analysis (EDA) employing Python, Pandas, and SQL to derive actionable insights from complex datasets.

🤖 Machine Learning Expertise: Proficient in the development and implementation of sophisticated machine learning algorithms, including: -Regression Techniques: Mastery of linear regression, logistic regression, and advanced regularisation techniques (Lasso, Ridge). -Classification Models: Skilled in deploying decision trees, random forests, support vector machines (SVM), and boosting methods (XGBoost, LightGBM) for predictive analytics. -Clustering and Segmentation: Experienced in K-means clustering, hierarchical clustering, and DBSCAN for market segmentation analysis. -Dimensionality Reduction: Proficient in PCA (Principal Component Analysis) and t-SNE for data visualisation and feature extraction.

📈 Advanced Statistical Analysis: Strong foundation in inferential statistics, hypothesis testing, and A/B testing, enabling robust data-driven decision-making in marketing and finance.

🧠 Deep Learning Applications: Experienced in designing and implementing neural networks using TensorFlow and Keras, including: -Convolutional Neural Networks (CNNs) for image processing tasks. -Recurrent Neural Networks (RNNs) and LSTMs (Long Short-Term Memory) for time series forecasting and natural language processing (NLP) applications. 📝 Natural Language Processing (NLP): Proficient in text analysis, sentiment evaluation, and language modelling to inform marketing strategies.

🎨 Data Visualisation and Communication: Skilled in utilising tools such as Matplotlib, Seaborn, and Tableau to create compelling visual representations of data, facilitating effective stakeholder communication.

💹 Financial Modelling and Analysis: Strong ability to construct financial models to evaluate business performance, profitability, and risk assessment.

🔍 Market Research and Consumer Insights:: Expertise in employing data analytics to uncover consumer behaviours, trends, and preferences, informing strategic marketing initiatives.

🌐 Big Data Technologies: Familiarity with Apache Spark and Hadoop for efficient processing and analysis of large-scale datasets.

🚀 Model Deployment and Optimisation: Experience in deploying machine learning models using Flask or FastAPI, with a strong emphasis on hyperparameter tuning and model performance enhancement.

🗄️ Database Management: Proficient in SQL and NoSQL databases, ensuring effective data storage and retrieval strategies.

💡 Critical Thinking and Problem-Solving: Exceptional analytical and logical reasoning skills, enabling the synthesis of complex data into meaningful insights.

🤝 Collaborative Communication: Excellent interpersonal skills, adept at conveying technical information to non-technical stakeholders in a clear and concise manner.

🛠️ Skills

Programming Languages

  • Python - SQL - JavaScript

Data Analysis & Visualization Tools

Python - Pandas - NumPy - Matplotlib - Seaborn - Power BI

Machine Learning & Deep Learning Frameworks

Machine Learning - Scikit-Learn - TensorFlow - Keras - PyTorch

Statistical Analysis

  • Statsmodels - SciPy

Financial & Business Analytics

  • Financial Modeling & Analysis - Time Series Analysis - Risk Management Techniques - Market Research & Consumer Behavior Analysis - Predictive Analytics

Soft Skills

  • Analytical Thinking - Problem Solving - Communication Skills - Team Collaboration - Project Management

📈 GitHub Stats

Salma's GitHub Stats

Visitors

GitHub Streak

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  1. EA-Stocks-Financial-Analysis-with-Prophet-Plotly EA-Stocks-Financial-Analysis-with-Prophet-Plotly Public

    I executed time series analysis with Facebook Prophet to forecast EA stock trends. Integrated Python and Plotly for interactive visualizations, illustrating stock price movements and patterns. The …

    Jupyter Notebook 3

  2. S-P-500-Stocks-Time-Series-Regression S-P-500-Stocks-Time-Series-Regression Public

    I conducted a time series regression analysis on S&P 500 stock data to identify trends and patterns affecting stock prices. Utilizing advanced statistical techniques and machine learning models, I …

    Jupyter Notebook 3

  3. Loan-Risk-Forecasting-ML-and-Financial-Analysis Loan-Risk-Forecasting-ML-and-Financial-Analysis Public

    I implemented machine learning techniques, 'Random Forest and XGBoost,' for loan status prediction. Utilized SHAP for interpretability, yielding insights into predictions. Conducted hyperparameter …

    Jupyter Notebook 3

  4. Youth-Suicide-Risk-Insights-ML-and-Visualization- Youth-Suicide-Risk-Insights-ML-and-Visualization- Public

    I leveraged Python libraries for data preprocessing and visualization. I applied machine learning models 'Random Forest and Logistic Regression' for risk prediction, with PCA and t-SNE for dimensi…

    Jupyter Notebook 3

  5. LLM-Prompt-Recovery-Fine-Tuning-T5-for-Success LLM-Prompt-Recovery-Fine-Tuning-T5-for-Success Public

    I leveraged NLP techniques by fine-tuning the T5 transformer model to recover LLM-generated prompts from transformed text data. I implemented a robust data preprocessing pipeline, optimizing the mo…

    Jupyter Notebook 2

  6. Predicting-Chatbot-Response-Preferences-with-LLMs Predicting-Chatbot-Response-Preferences-with-LLMs Public

    I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding…

    Jupyter Notebook 2