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Schedule Interview NowBillal P. Data Analytics, SQL and Machine Learning
My name is Billal P. and I have over 3 years of experience in the tech industry. I specialize in the following technologies: LaTeX, SAP CRM, MATLAB, Office 365, Price & Quote Negotiation, etc.. I hold a degree in Master's degree, Bachelor's degree. Some of the notable projects I’ve worked on include: ECOMERCE Sales Analysis, Crop Treatment Recommendation System, Course: Python in Operation: Data Structures and Algorithms, Analysis and Prediction of Tesla Stock Prices Using Neural Networks, Data Science Models and their Application, etc.. I am based in Karachi, Pakistan. I've successfully completed 10 projects while developing at Softaims.
My expertise lies in deeply understanding and optimizing solution performance. I have a proven ability to profile systems, analyze data access methods, and implement caching strategies that dramatically reduce latency and improve responsiveness under load. I turn slow systems into high-speed performers.
I focus on writing highly efficient, clean, and well-documented code that minimizes resource consumption without sacrificing functionality. This dedication to efficiency is how I contribute measurable value to Softaims’ clients by reducing infrastructure costs and improving user satisfaction.
I approach every project with a critical eye for potential bottlenecks, proactively designing systems that are efficient from the ground up. I am committed to delivering software that sets the standard for speed and reliability.
Main technologies
Data Analytics, SQL and Machine Learning
3 years
LaTeX
1 Year
SAP CRM
2 Years
MATLAB
2 Years
Additional skills
Direct hire
Potentially possible
Previous Company
Techlogix
Experience Highlights
ECOMERCE Sales Analysis
E-commerce Data Visualization Project on Power BI: This project focuses on analyzing e-commerce data to derive key business insights through interactive visualizations. It identifies the best-performing product category, determines the most profitable customer location, and highlights the top customer based on purchasing behavior. Additionally, the project formulates research topics based on emerging trends and patterns in the data. By leveraging data visualization techniques, businesses can make informed decisions to optimize sales strategies and improve overall performance.
Crop Treatment Recommendation System
This project provides farmers with treatment recommendations based on the problem they're facing with their crops, using machine learning. Random data generates for farmers. The data included crops, problem, seasons etc. The target was the recommendations. Random forest classifier was used to train on the data . Then an app is made with streamlit. subequently it is also shared on github.
Course: Python in Operation: Data Structures and Algorithms
This course is advance course for python developers and Data Science Students
Analysis and Prediction of Tesla Stock Prices Using Neural Networks
1. Data Preparation Loading, Transformation, Cleaning Feature and Target Splitting Train-Test Split Feature Scaling 2. Neural Network Model 1: Consists of two layers with 32 and 16 units, respectively. Model 2: More complex, with three layers containing 64, 32, and 16 units. Training: 50 epochs, with validation. 3.Model Evaluation MSE Loss Curves K-Fold Cross-Validation Summary The code analyze and predict Tesla stock prices. It uses feature extraction, model training, and cross-validation to ensure accurate predictions and generalizability on recent data.
Data Science Models and their Application
The presentation titled "Data Science Models - Introduction and Applications," focusing on the various types of data science models, their purposes, and applications. It covers fundamental categories of models, including supervised and unsupervised models, and details specific types of models used for regression, classification, clustering, and dimensionality reduction. The presentation also provides examples of model suitability based on dataset characteristics such as size, quality, and feature types, making it a comprehensive guide for understanding data science models applications.
Education
Universität Siegen
Master's degree in Engineering physics
2013-01-01-2016-01-01
NED University of Engineering and Technology
Bachelor's degree in Mechanical engineering
2006-01-01-2010-01-01
Languages
- German
- English
- Hindi
- Urdu
Github