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Fatima A.

@NeuralNexus16

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AI/ML ✅ Data Science ✅ Full-Stack Web ✅

$5 USD / giờ
・
Cờ của
Pakistan (8:00 SA)
・
Đã tham gia vào tháng 1 15, 2025
$5 USD / giờ
・
⭐ Empower Your Business with AI, Machine Learning, and Web Solutions! I'm Fatima, your reliable expert in AI, Machine Learning, and Full-Stack Web Solutions. I specialize in transforming data into actionable insights while creating seamless, scalable systems that deliver results. ✅ AI/ML Specialist & Data Scientist ✅ Expert in Web-Based Predictive Analytics and AI Integration ✅ Successfully Completed 10+ High-Impact Projects ✅ Committed to Delivering Exceptional Client Satisfaction Why Work With Me? With diverse experience spanning healthcare, education, finance, and technology, I deliver customized AI-driven solutions that unlock the power of data. From integrating AI models into web platforms to deploying advanced analytics, I’m here to help you stay ahead in a competitive digital world. My Expertise: AI, Data Science & Machine Learning: ✅ Predictive Analytics & Modeling ✅ Deep Learning & Neural Networks ✅ Exploratory Data Analysis (EDA) ✅ Supervised & Unsupervised Learning ✅ Model Deployment Web Solutions: ✅ AI Model Integration with Web Applications ✅ Custom APIs for AI/ML Solutions ✅ Scalable, Data-Driven Web Platforms Data Visualization: ✅ Interactive Dashboards ✅ Infographics & Visual Analytics Tools & Technologies: Programming & ML Frameworks: ✅ Python, TensorFlow, Keras, PyTorch, Scikit-learn ✅ XGBoost, LightGBM, Pandas, NumPy Visualization: ✅ Tableau, Power BI, Matplotlib, Plotly Web Development & Deployment: ✅ Flask, Django, Streamlit, FastAPI ✅ Google AI Platform, AWS SageMaker, Microsoft Azure Ready to Get Started? Let’s work together to build innovative AI, data, and web solutions tailored to your needs. Message me today, and let’s bring your vision to life! Best Regards, Fatima
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Freelancer C Programmers Pakistan
Xem tất cả về C Programmers tại Pakistan

Portfolio

Portfolio

12465757
⭐ Unveiling Key Insights from High-Energy Physics Research This project analyzed the citation network and abstract data of high-energy physics research papers to identify influential works, emerging themes, and structural patterns. Key Contributions: ✅ Citation Network Analysis: Identified the top 10 most-cited papers and influential nodes using citation counts and PageRank scores. Analyzed citation trends over time, revealing peaks in research activity during the late 1990s and early 2000s. Visualized the citation network to highlight key research hubs and their relationships. ✅ Abstract Analysis: Performed keyword frequency analysis to uncover dominant research themes such as theoretical physics, string theory, and quantum field models. Generated a word cloud to visualize prominent terms and themes. ✅ Data Visualization: Visualized citation trends, network structures, and keyword distributions using graphs and word clouds. ✅ Tools & Techniques: Python (NetworkX, Matplotlib,
Citation Network & Abstract Analysis
12465753
⭐ Improving Public Health Outcomes with Data Analytics This project focused on analyzing Uganda’s health indicators to identify key factors affecting public health and using predictive modeling to forecast life expectancy at age 60. Key Contributions: ✅ Dataset Preparation: Cleaned and preprocessed health indicator data from WHO, including handling missing values, detecting outliers, and converting data types for analysis. Aggregated data for time-series forecasting and ensured data integrity for robust insights. ✅ Exploratory Data Analysis (EDA): Analyzed trends in life expectancy, maternal health, and nutrition using visualizations like line plots, boxplots, and correlation matrices. Identified strong correlations between maternal care and child survival, and gender disparities in life expectancy. ✅ Predictive Modeling: Built and optimized a Gradient Boosting model to predict life expectancy, achieving an RMSE of 0.039 and R² of 0.99. Conducted feature importance analysis to ide
Health Data Analysis and Predictive
⭐ Improving Public Health Outcomes with Data Analytics This project focused on analyzing Uganda’s health indicators to identify key factors affecting public health and using predictive modeling to forecast life expectancy at age 60. Key Contributions: ✅ Dataset Preparation: Cleaned and preprocessed health indicator data from WHO, including handling missing values, detecting outliers, and converting data types for analysis. Aggregated data for time-series forecasting and ensured data integrity for robust insights. ✅ Exploratory Data Analysis (EDA): Analyzed trends in life expectancy, maternal health, and nutrition using visualizations like line plots, boxplots, and correlation matrices. Identified strong correlations between maternal care and child survival, and gender disparities in life expectancy. ✅ Predictive Modeling: Built and optimized a Gradient Boosting model to predict life expectancy, achieving an RMSE of 0.039 and R² of 0.99. Conducted feature importance analysis to ide
Health Data Analysis and Predictive
12465729
⭐ Enhancing Marketing Strategies with Clustering Analysis This project focused on segmenting mall customers into meaningful groups using clustering algorithms to aid in targeted marketing strategies. Key Contributions: ✅ Data Exploration: Analyzed customer demographics, annual income, and spending scores to identify patterns and relationships. ✅ Clustering Model Development: Built and optimized K-Means clustering models to segment customers. Determined the optimal number of clusters using Elbow Method and Silhouette Score (k=5 for 2 features, k=6 for 3 features). ✅ Feature Engineering: Focused on key attributes: Age, Annual_Income, and Spending_Score to enhance clustering effectiveness. ✅ Evaluation & Visualization: Visualized clusters with scatter plots and provided actionable insights for improving marketing strategies. ✅ Tools & Techniques: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn), KMeans clustering, and feature scaling. Outcome: This project provided a s
Mall Customer Segmentation
12465740
⭐ Enhancing Customer Targeting with Clustering This project involved segmenting mall customers into meaningful groups to optimize marketing strategies using clustering algorithms. Key Contributions: ✅ Data Exploration: Analyzed customer demographics, annual income, and spending scores to identify patterns. ✅ Clustering Model Development: Built K-Means clustering models and determined the optimal number of clusters using Elbow and Silhouette methods. Visualized clusters with scatter plots for insights. ✅ Feature Engineering: Focused on key attributes (Age, Annual_Income, Spending_Score) to refine clustering. ✅ Tools & Techniques: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn). Outcome: Developed actionable segmentation for personalized marketing and enhanced customer engagement. Project: Shopify Store Data Cleaning and Dashboard Design ⭐ Optimizing E-Commerce Data for Insights This project focused on cleaning and analyzing Shopify store datasets to derive valuable bu
Mall Customer Segmentation
⭐ Enhancing Customer Targeting with Clustering This project involved segmenting mall customers into meaningful groups to optimize marketing strategies using clustering algorithms. Key Contributions: ✅ Data Exploration: Analyzed customer demographics, annual income, and spending scores to identify patterns. ✅ Clustering Model Development: Built K-Means clustering models and determined the optimal number of clusters using Elbow and Silhouette methods. Visualized clusters with scatter plots for insights. ✅ Feature Engineering: Focused on key attributes (Age, Annual_Income, Spending_Score) to refine clustering. ✅ Tools & Techniques: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn). Outcome: Developed actionable segmentation for personalized marketing and enhanced customer engagement. Project: Shopify Store Data Cleaning and Dashboard Design ⭐ Optimizing E-Commerce Data for Insights This project focused on cleaning and analyzing Shopify store datasets to derive valuable bu
Mall Customer Segmentation
12465717
⭐ Optimizing Employee Retention with Data Science This project focused on identifying key factors driving employee attrition and developing a predictive model to help the organization proactively address retention challenges. Key Contributions: ✅ Data Exploration & Analysis: Conducted univariate, bivariate, and multivariate analyses to identify patterns in employee demographics, job roles, and satisfaction metrics contributing to attrition. ✅ Feature Engineering: Processed data with scaling, encoding categorical variables, and addressing multicollinearity for robust modeling. ✅ Model Development: Built Logistic Regression and SVM models to predict employee attrition, achieving 93% accuracy with an RBF kernel in SVM while focusing on maximizing recall for classifying at-risk employees. ✅ Visualization & Insights: Provided actionable insights through heatmaps, correlation analysis, and attrition pattern visualization to guide targeted retention strategies. ✅ Tools & Techniq
Employee Attrition Predictor
⭐ Optimizing Employee Retention with Data Science This project focused on identifying key factors driving employee attrition and developing a predictive model to help the organization proactively address retention challenges. Key Contributions: ✅ Data Exploration & Analysis: Conducted univariate, bivariate, and multivariate analyses to identify patterns in employee demographics, job roles, and satisfaction metrics contributing to attrition. ✅ Feature Engineering: Processed data with scaling, encoding categorical variables, and addressing multicollinearity for robust modeling. ✅ Model Development: Built Logistic Regression and SVM models to predict employee attrition, achieving 93% accuracy with an RBF kernel in SVM while focusing on maximizing recall for classifying at-risk employees. ✅ Visualization & Insights: Provided actionable insights through heatmaps, correlation analysis, and attrition pattern visualization to guide targeted retention strategies. ✅ Tools & Techniq
Employee Attrition Predictor
12465709
This project focused on analyzing noise generated by UAV propellers with varying blade spacing angles and axial offsets, aiming to develop quieter UAV designs. Key Objectives: ✅ Measured and analyzed noise levels generated by UAV propellers. ✅ Studied the effects of varying blade spacing angles and axial offsets on noise reduction. ✅ Utilized advanced data analysis techniques, such as FFT and A-weighting, to extract meaningful insights from noise data. Contributions: Designed and manufactured UAV propellers with different configurations using 3D printing. Conducted noise measurements using acoustic sensors and microphones in controlled setups. Analyzed sound pressure levels (SPL) and frequency spectra to identify optimal configurations. Tools & Techniques: Simulation Tools: ANSYS Fluent, OpenFOAM for aerodynamic and acoustic analysis. Data Analysis: Python libraries (NumPy, SciPy) for FFT and signal processing. Equipment: Microphone arrays, accelerometers, and environmental senso
UAV Propeller Noise Analysis
This project focused on analyzing noise generated by UAV propellers with varying blade spacing angles and axial offsets, aiming to develop quieter UAV designs. Key Objectives: ✅ Measured and analyzed noise levels generated by UAV propellers. ✅ Studied the effects of varying blade spacing angles and axial offsets on noise reduction. ✅ Utilized advanced data analysis techniques, such as FFT and A-weighting, to extract meaningful insights from noise data. Contributions: Designed and manufactured UAV propellers with different configurations using 3D printing. Conducted noise measurements using acoustic sensors and microphones in controlled setups. Analyzed sound pressure levels (SPL) and frequency spectra to identify optimal configurations. Tools & Techniques: Simulation Tools: ANSYS Fluent, OpenFOAM for aerodynamic and acoustic analysis. Data Analysis: Python libraries (NumPy, SciPy) for FFT and signal processing. Equipment: Microphone arrays, accelerometers, and environmental senso
UAV Propeller Noise Analysis
12465757
⭐ Unveiling Key Insights from High-Energy Physics Research This project analyzed the citation network and abstract data of high-energy physics research papers to identify influential works, emerging themes, and structural patterns. Key Contributions: ✅ Citation Network Analysis: Identified the top 10 most-cited papers and influential nodes using citation counts and PageRank scores. Analyzed citation trends over time, revealing peaks in research activity during the late 1990s and early 2000s. Visualized the citation network to highlight key research hubs and their relationships. ✅ Abstract Analysis: Performed keyword frequency analysis to uncover dominant research themes such as theoretical physics, string theory, and quantum field models. Generated a word cloud to visualize prominent terms and themes. ✅ Data Visualization: Visualized citation trends, network structures, and keyword distributions using graphs and word clouds. ✅ Tools & Techniques: Python (NetworkX, Matplotlib,
Citation Network & Abstract Analysis
12465753
⭐ Improving Public Health Outcomes with Data Analytics This project focused on analyzing Uganda’s health indicators to identify key factors affecting public health and using predictive modeling to forecast life expectancy at age 60. Key Contributions: ✅ Dataset Preparation: Cleaned and preprocessed health indicator data from WHO, including handling missing values, detecting outliers, and converting data types for analysis. Aggregated data for time-series forecasting and ensured data integrity for robust insights. ✅ Exploratory Data Analysis (EDA): Analyzed trends in life expectancy, maternal health, and nutrition using visualizations like line plots, boxplots, and correlation matrices. Identified strong correlations between maternal care and child survival, and gender disparities in life expectancy. ✅ Predictive Modeling: Built and optimized a Gradient Boosting model to predict life expectancy, achieving an RMSE of 0.039 and R² of 0.99. Conducted feature importance analysis to ide
Health Data Analysis and Predictive
⭐ Improving Public Health Outcomes with Data Analytics This project focused on analyzing Uganda’s health indicators to identify key factors affecting public health and using predictive modeling to forecast life expectancy at age 60. Key Contributions: ✅ Dataset Preparation: Cleaned and preprocessed health indicator data from WHO, including handling missing values, detecting outliers, and converting data types for analysis. Aggregated data for time-series forecasting and ensured data integrity for robust insights. ✅ Exploratory Data Analysis (EDA): Analyzed trends in life expectancy, maternal health, and nutrition using visualizations like line plots, boxplots, and correlation matrices. Identified strong correlations between maternal care and child survival, and gender disparities in life expectancy. ✅ Predictive Modeling: Built and optimized a Gradient Boosting model to predict life expectancy, achieving an RMSE of 0.039 and R² of 0.99. Conducted feature importance analysis to ide
Health Data Analysis and Predictive
12465729
⭐ Enhancing Marketing Strategies with Clustering Analysis This project focused on segmenting mall customers into meaningful groups using clustering algorithms to aid in targeted marketing strategies. Key Contributions: ✅ Data Exploration: Analyzed customer demographics, annual income, and spending scores to identify patterns and relationships. ✅ Clustering Model Development: Built and optimized K-Means clustering models to segment customers. Determined the optimal number of clusters using Elbow Method and Silhouette Score (k=5 for 2 features, k=6 for 3 features). ✅ Feature Engineering: Focused on key attributes: Age, Annual_Income, and Spending_Score to enhance clustering effectiveness. ✅ Evaluation & Visualization: Visualized clusters with scatter plots and provided actionable insights for improving marketing strategies. ✅ Tools & Techniques: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn), KMeans clustering, and feature scaling. Outcome: This project provided a s
Mall Customer Segmentation
12465740
⭐ Enhancing Customer Targeting with Clustering This project involved segmenting mall customers into meaningful groups to optimize marketing strategies using clustering algorithms. Key Contributions: ✅ Data Exploration: Analyzed customer demographics, annual income, and spending scores to identify patterns. ✅ Clustering Model Development: Built K-Means clustering models and determined the optimal number of clusters using Elbow and Silhouette methods. Visualized clusters with scatter plots for insights. ✅ Feature Engineering: Focused on key attributes (Age, Annual_Income, Spending_Score) to refine clustering. ✅ Tools & Techniques: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn). Outcome: Developed actionable segmentation for personalized marketing and enhanced customer engagement. Project: Shopify Store Data Cleaning and Dashboard Design ⭐ Optimizing E-Commerce Data for Insights This project focused on cleaning and analyzing Shopify store datasets to derive valuable bu
Mall Customer Segmentation
⭐ Enhancing Customer Targeting with Clustering This project involved segmenting mall customers into meaningful groups to optimize marketing strategies using clustering algorithms. Key Contributions: ✅ Data Exploration: Analyzed customer demographics, annual income, and spending scores to identify patterns. ✅ Clustering Model Development: Built K-Means clustering models and determined the optimal number of clusters using Elbow and Silhouette methods. Visualized clusters with scatter plots for insights. ✅ Feature Engineering: Focused on key attributes (Age, Annual_Income, Spending_Score) to refine clustering. ✅ Tools & Techniques: Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn). Outcome: Developed actionable segmentation for personalized marketing and enhanced customer engagement. Project: Shopify Store Data Cleaning and Dashboard Design ⭐ Optimizing E-Commerce Data for Insights This project focused on cleaning and analyzing Shopify store datasets to derive valuable bu
Mall Customer Segmentation
12465717
⭐ Optimizing Employee Retention with Data Science This project focused on identifying key factors driving employee attrition and developing a predictive model to help the organization proactively address retention challenges. Key Contributions: ✅ Data Exploration & Analysis: Conducted univariate, bivariate, and multivariate analyses to identify patterns in employee demographics, job roles, and satisfaction metrics contributing to attrition. ✅ Feature Engineering: Processed data with scaling, encoding categorical variables, and addressing multicollinearity for robust modeling. ✅ Model Development: Built Logistic Regression and SVM models to predict employee attrition, achieving 93% accuracy with an RBF kernel in SVM while focusing on maximizing recall for classifying at-risk employees. ✅ Visualization & Insights: Provided actionable insights through heatmaps, correlation analysis, and attrition pattern visualization to guide targeted retention strategies. ✅ Tools & Techniq
Employee Attrition Predictor
⭐ Optimizing Employee Retention with Data Science This project focused on identifying key factors driving employee attrition and developing a predictive model to help the organization proactively address retention challenges. Key Contributions: ✅ Data Exploration & Analysis: Conducted univariate, bivariate, and multivariate analyses to identify patterns in employee demographics, job roles, and satisfaction metrics contributing to attrition. ✅ Feature Engineering: Processed data with scaling, encoding categorical variables, and addressing multicollinearity for robust modeling. ✅ Model Development: Built Logistic Regression and SVM models to predict employee attrition, achieving 93% accuracy with an RBF kernel in SVM while focusing on maximizing recall for classifying at-risk employees. ✅ Visualization & Insights: Provided actionable insights through heatmaps, correlation analysis, and attrition pattern visualization to guide targeted retention strategies. ✅ Tools & Techniq
Employee Attrition Predictor
12465709
This project focused on analyzing noise generated by UAV propellers with varying blade spacing angles and axial offsets, aiming to develop quieter UAV designs. Key Objectives: ✅ Measured and analyzed noise levels generated by UAV propellers. ✅ Studied the effects of varying blade spacing angles and axial offsets on noise reduction. ✅ Utilized advanced data analysis techniques, such as FFT and A-weighting, to extract meaningful insights from noise data. Contributions: Designed and manufactured UAV propellers with different configurations using 3D printing. Conducted noise measurements using acoustic sensors and microphones in controlled setups. Analyzed sound pressure levels (SPL) and frequency spectra to identify optimal configurations. Tools & Techniques: Simulation Tools: ANSYS Fluent, OpenFOAM for aerodynamic and acoustic analysis. Data Analysis: Python libraries (NumPy, SciPy) for FFT and signal processing. Equipment: Microphone arrays, accelerometers, and environmental senso
UAV Propeller Noise Analysis
This project focused on analyzing noise generated by UAV propellers with varying blade spacing angles and axial offsets, aiming to develop quieter UAV designs. Key Objectives: ✅ Measured and analyzed noise levels generated by UAV propellers. ✅ Studied the effects of varying blade spacing angles and axial offsets on noise reduction. ✅ Utilized advanced data analysis techniques, such as FFT and A-weighting, to extract meaningful insights from noise data. Contributions: Designed and manufactured UAV propellers with different configurations using 3D printing. Conducted noise measurements using acoustic sensors and microphones in controlled setups. Analyzed sound pressure levels (SPL) and frequency spectra to identify optimal configurations. Tools & Techniques: Simulation Tools: ANSYS Fluent, OpenFOAM for aerodynamic and acoustic analysis. Data Analysis: Python libraries (NumPy, SciPy) for FFT and signal processing. Equipment: Microphone arrays, accelerometers, and environmental senso
UAV Propeller Noise Analysis
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