I’m working as a freelancer of Data analyst and scientist on many projects. Specifically, I’m working in the field of advanced analytics and artificial intelligence while utilizing cutting-edge data science approaches li…
I’m working as a freelancer of Data analyst and scientist on many projects. Specifically, I’m working in the field of advanced analytics and artificial intelligence while utilizing cutting-edge data science approaches like machine learning and deep learning. Experience in data pre-processing, predictive modeling using machine learning and deep learning approaches, statistics, designing data-driven applications and overcoming challenging architecture across several sectors. skilled in data science-related programming languages such as Python, Java, SQL ,…. Delivery involvement spans Healthcare, Financial Services, Public Sector, Natural Language Processing and Computer Vision. I am now looking to excel in a new position within a market leading company as a senior data scientist in an organization, wherein, my technical efficiency and commitment will contribute towards the organizational as well as personal growth.
Data Cleaning and Preprocessing
-Machine Learning and AI Development.
-Data Visualization and Communication.
- Continuous Learning and Research.
Collaboration with stockholder in different domains , including business leaders, domain experts, and IT teams
Analyzed Saudi Telecom customers' sentiments based on tweets to the company's customer care account.
Used the 'customer care tweets KSA' dataset from Kaggle in the Arabic language.
Translated text into English using the googletrans library before annotation.
Annotated sentiments using TextBlob due to the lack of Arabic labeling resources and the absence of sentiment labels in the original dataset.
Cleaned text by removing non-alphabetic characters, links, and usernames.
Split text into tokens.
Lemmatized tokens to get their base forms.
Applied TF-IDF for feature extraction.
Employed Random Forest classifications for sentiment analysis.
Calculated accuracy and confusion matrices to assess classifier performance.
Analyzed public airline sentiment based on the 'Twitter US Airline Sentiment' dataset available in Kaggle.
Performed labeling using TextBlob Data Labeling.
Removed special characters, digits, URLs, and Twitter identities during preprocessing.
Changed all text to lowercase for uniformity.
Tokenized tweets into separate words or tokens.
Lemmatized tokens to return them to their original forms.
Used TF-IDF to vectorize preprocessed text into numerical data.
Used classification models including Nave Base, logistic regression, SVM, neural networks, and Random Forest.
Used accuracy to determine classifier performance in predicting tweet sentiments.
Imported essential Python libraries such as pandas, numpy, and statsmodels.
Plotted the time series to understand trend, seasonality, and patterns.
Used the seasonal_decompose function to break down the time series into trend, seasonality, and residuals.
Used statistical tests such as the Augmented Dickey-Fuller test to check stationarity.
Determined the orders for the SARIMAX model.
Visualized the data with statistical functions.
Created a model for predicting churn to help telecom carriers identify consumers most likely to leave.
Used machine learning techniques including Decision Tree, Random Forest, and SVM.
Obtained the best results by applying Random Forest algorithm.
Demonstrated good accuracy in testing the suggested churn predictive model's classification algorithm.
Collected CSI data for seven distinct human everyday activities.
Transformed CSI data into images.
Used the resulting images as inputs for a 2D Convolutional Neural Network (CNN) classifier.
Demonstrated good accuracy in testing the suggested CSI-based model.
Proposed a two-tier feature selection method to enhance structural-based alert correlation.
Ranked features based on information gain entropy in decreasing order.
Added additional discriminative features beyond the initial ranking.
Evaluated the model using the 2000 DARPA intrusion detection scenario-specific dataset.
Aimed to improve the accuracy of alert correlation.
Tested the proposed model using DARPA 2000 and ISCX2012 datasets.
Evaluated correlation effectiveness using Completeness and Soundness metrics.
Used Convolutional Neural Networks (CNN) to detect objects in the environment.
Used TensorFlow Object Detection API to create, test, and use object detection models.
Included a person and a During in the dataset for the study.
Labeled images manually using Labeling, a graphical image annotation tool.
Exported the inference graph after training.
Used the inference graph containing the object detection classifier.
Transformed CSI data into images and used the resulting images as inputs for a 2D Convolutional Neural Network (CNN) classifier.
Demonstrated good accuracy in testing the suggested model.
Analyzed data and data science in the area of stock market prediction for the next few days.
Conducted interviews with stakeholders to gather input and expectations towards data requirements.
Established the organizational design and timelines to support data analysis operations.
Understood existing processes and business requirements to formulate data modeling recommendations.
Developed a custom tool to help executives and business teams prioritize analytics business use cases.
Visualized the data with statistical functions using matplot library.
Pre-processed data with data cleaning, normalization, handling missing values, and other data transformation techniques.
Combined the Discrete Wavelet Transform (DWT) and Long Short-Term Memory (LSTM) to estimate stock prices in the market for the next few days.
Performed data analysis and data science analysis in the area of CT-image classification of covid-19.
Understood technical requirement and data collection.
Built deep learning model using transfer learning approach (VGG16, VGG19) to predict CT images of Covid 19.
Identified issues, analyzed information, and provided solutions to problems.
Performed image pre-processing including crops, resizing, scaling, and augmentation.
Performed teaching, supervision, and administration work.
Taught Geographical user interface (GIS).
Taught Data Mining and artificial intelligent.
Taught Database Management system.
Taught Programming languages.
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