You will get machine learning, deep learning and data science projects using python
Project details
Hello Everyone!
Data is a company's most valuable asset. Better data leads to better decision-making. Data science offers the tools necessary to analyze and understand your data. This field encompasses various stages, including data preparation and exploration, data representation and transformation, data visualization, presentation, and predictive analytics.
If you need assistance in transforming your messy data into valuable, actionable insights, I'm here to help you.
My Skill list for the Projects is...
Programming Skills:
> Python
> Numpy
> Pandas
> Matplotlib
> Seaborn
> Scikit-learn
Data Preprocessing:
> Dealing with missing data
> Data Imputation
> Label Encoding for Classification
> Handling Categorical Data
> Techniques of feature transformation
> Feature Engineering
Data Visualization:
> Matplotlib
> Plotly
> Seaborn
> Folium
Machine Learning:
> Linear Regression
> Logistic Regression
> K-nearest neighbour (KNN)
> Decision Tree Classifier
> Random Forest Classifier
> Support Vector Machine
> Bayesian Algorithm
Deep Learning:
> Artificial Neural Networks (ANNs)
> Convolutional Neural Networks (CNNs)
Model Deployment:
> Flask
Data is a company's most valuable asset. Better data leads to better decision-making. Data science offers the tools necessary to analyze and understand your data. This field encompasses various stages, including data preparation and exploration, data representation and transformation, data visualization, presentation, and predictive analytics.
If you need assistance in transforming your messy data into valuable, actionable insights, I'm here to help you.
My Skill list for the Projects is...
Programming Skills:
> Python
> Numpy
> Pandas
> Matplotlib
> Seaborn
> Scikit-learn
Data Preprocessing:
> Dealing with missing data
> Data Imputation
> Label Encoding for Classification
> Handling Categorical Data
> Techniques of feature transformation
> Feature Engineering
Data Visualization:
> Matplotlib
> Plotly
> Seaborn
> Folium
Machine Learning:
> Linear Regression
> Logistic Regression
> K-nearest neighbour (KNN)
> Decision Tree Classifier
> Random Forest Classifier
> Support Vector Machine
> Bayesian Algorithm
Deep Learning:
> Artificial Neural Networks (ANNs)
> Convolutional Neural Networks (CNNs)
Model Deployment:
> Flask
Machine Learning Tools
Keras, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Tableau, TensorFlowWhat's included
| Service Tiers |
Starter
$10
|
Standard
$20
|
Advanced
$30
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 1 | 3 | 5 |
Number of Graphs/Charts | 5 | 7 | 15 |
Model Validation/Testing | |||
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code |
Frequently asked questions
About Muhammad
AI & Machine Learning Engineer
Khanewal, Pakistan - 6:54 am local time
Skills:
- Proficient in Python, NLP, Deep learning, predictive modeling.
- Skilled in Sklearn, Keras, Matplotlib, Seaborn, Pandas, Numpy.
- Experience with remote work environments
- Strong problem-solving and analytical skills
- Effective communication and teamwork abilities
Work Experience:
-Completed a two-month data science internship at Prodigy Infotech, developing predictive models
that increased customer retention by 15%.
-Conducted a two-month data science internship at Oasis Infobyte, optimizing algorithms that
improved data processing speed by 30%.
-Executed a two-month data science internship at Octanet, implementing machine learning solutions
that enhanced project efficiency by 20%.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Consultation and Requirement Gathering
In this initial step, I will have a detailed discussion with the client to understand their business needs, project goals, data availability, and specific requirements. This ensures that the project's scope is well-defined.