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Billal P. - Fullstack Developer, Project Budget, Machine Learning

My journey at Softaims has been defined by curiosity, growth, and collaboration. I’ve always believed that good software is not just built—it’s carefully shaped through understanding, exploration, and iteration. Every project I’ve worked on has taught me something new about how to balance simplicity with depth, and efficiency with creativity. At its core, my work revolves around helping businesses and people achieve more through thoughtful technology. I’ve learned that the most successful projects come from teams that communicate openly and stay adaptable. At Softaims, I’ve had the opportunity to work alongside professionals who challenge assumptions, share knowledge generously, and inspire continuous improvement. I take pride in focusing on the fundamentals—clarity in logic, consistency in design, and empathy in execution. Software is more than a set of features; it’s a reflection of how we think about problems and how we choose to solve them. By maintaining this perspective, I aim to build solutions that are not only effective today but also flexible enough to support the challenges of tomorrow. The culture at Softaims promotes learning as an ongoing process. Every new project feels like a step forward, both personally and professionally. I see each challenge as a chance to refine my skills and contribute to the shared vision of building technology that genuinely improves lives.

Main technologies

  • Fullstack Developer

    3 years

  • LaTeX

    2 Years

  • SAP CRM

    2 Years

  • MATLAB

    1 Year

Additional skills

  • LaTeX
  • SAP CRM
  • MATLAB
  • Office 365
  • Price & Quote Negotiation
  • SQL
  • Python
  • Data Analytics
  • Forecasting
  • Management Skills
  • Physics
  • Autodesk AutoCAD
  • Project Budget
  • Machine Learning

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Potentially possible

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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

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