You will get a state-of-the-art (SOTA) machine learning model
Project details
I will train machine learning and deep learning algorithm for:
Classification
Regression
I have experience in:
Linear Regression
Logistic Regression
Decision Tree
Random Forest
SVM
Neural Network
Convolutional Neural Network
Programming Languages and Libraries I'm confident in:
Python
Tensorflow
Pytorch
Sklearn
Numpy
Pandas
Seaborn
Matplotlib
Flask
Let's get started to assist you well.
Classification
Regression
I have experience in:
Linear Regression
Logistic Regression
Decision Tree
Random Forest
SVM
Neural Network
Convolutional Neural Network
Programming Languages and Libraries I'm confident in:
Python
Tensorflow
Pytorch
Sklearn
Numpy
Pandas
Seaborn
Matplotlib
Flask
Let's get started to assist you well.
What's included
| Service Tiers |
Starter
$70
|
Standard
$80
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 2 | 3 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$10 - $30
Additional Revision
+$10
Additional Model Variation
(+ 2 Days)
+$20
Additional Scenario
(+ 2 Days)
+$20
Additional Graph/Chart
(+ 2 Days)
+$15
Model Documentation
(+ 2 Days)
+$20
Data Source Connectivity
(+ 3 Days)
+$30Frequently asked questions
About Muneeb
AI & Machine Learning | Data Analytics
Peshawar, Pakistan - 9:45 pm local time
I’m a Data Analytics and AI Specialist with over 3 years of experience in leveraging machine learning, computer vision, and deep learning to solve complex problems. With strong analytical and programming skills, I'm seeking both short-term and long-term opportunities in innovative projects.
🛠️ Skills
✔️ Data Analysis and Visualization
✔️ Machine Learning Algorithms
✔️ Deep Learning Techniques
✔️ Generative Models
✔️ Image Processing and Computer Vision
✔️ Object Detection and Tracking
✔️ Feature Engineering
✔️ Model Evaluation and Optimization
✔️ Statistical Analysis
✔️ Data Preprocessing and Cleaning
🧑🏻💻 Languages & Tools
✔️ Python
✔️ R (for statistical analysis)
✔️ SQL (for database querying)
✔️ PyTorch
✔️ TensorFlow
✔️ OpenCV
✔️ Scikit-learn
✔️ Matplotlib / Seaborn (for data visualization)
✔️ Google Colab
✔️ Jupyter Notebook
✔️ GitHub
✔️ VS Code
🚀 Projects
✔️ Sales Data Analysis: Analyzed sales data using Python and SQL to identify trends and insights.
✔️ Customer Segmentation: Utilized clustering algorithms to segment customers based on purchasing behavior.
✔️ COVID-19 Data Visualization: Created visualizations to track COVID-19 cases and vaccination rates using Matplotlib and Seaborn.
✔️ Predictive Sales Forecasting: Developed a machine learning model to predict future sales using historical data.
✔️ Marketing Campaign Analysis: Analyzed the effectiveness of marketing campaigns through A/B testing and data visualization.
✔️ Churn Prediction Model: Created a model to predict customer churn based on user behavior data.
✔️ Social Media Sentiment Analysis: Analyzed sentiment from social media data to gauge public opinion on various topics.
✔️ Financial Risk Assessment: Conducted a risk assessment project using financial data to identify high-risk customers.
🥇 Achievements
🔰 Google Certified Data Analyst
🔰 DAFI Scholar (2019-2022)
🔰 Aspire Leaders Scholar (Cycle 1, 2023)
Steps for completing your project
After purchasing the project, send requirements so Muneeb can start the project.
Delivery time starts when Muneeb receives requirements from you.
Muneeb works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preprocessing
Data preprocessing is a method for transforming unclean data into clean data sets. In other words, anytime data is acquired from various sources, it is done so in a raw manner that makes analysis impossible.
Feature Engineering
Feature creation, transformations, feature extraction, and feature selection are the four key processes of feature engineering in machine learning.